DEV Community: ANIRUDDHA ADAK

Under the Hood of 348+ Code Merges: My Open Source Evolution

ANIRUDDHA ADAK — Sun, 21 Jun 2026 07:58:08 +0000

Hey, I am ANIRUDDHA ADAK. Over the past few years, I have been building in the open. When I first started on New Year's Day of 2024, I was just trying to figure out how Git worked. Today, with 348+ merged pull requests across dozens of repositories, I can say that open source has shaped who I am as a developer.

I want to share my complete contribution history. In this document, you will find all my pull requests merged across various repositories, starting from my most recent contributions to my very first one. This timeline shows my progress from practicing basic Python scripts to resolving Windows system bugs and working on advanced Graph RAG tools like cognee.

Codebases I Contributed To

Here is a complete list of the external projects where I spent my time and merged code.

topoteretes/cognee
openclaw/openclaw
HSF/hsf.github.io
Tracer-Cloud/opensre
NousResearch/hermes-agent
vgvassilev/clad
accomplish-ai/coworker
google-gemini/gemini-cli
GetStream/Vision-Agents
RocketChat/Rocket.Chat
sumanth-0/100LinesOfPythonCode
TheAlgorithms/Python
brisbanesocialchess/awesome-social-chess
agnilondapakou/helloWorld
brisbanesocialchess/.github
bratergit/lastChanceHackertoberfest
dipandhali2021/CineMagic
Abinash6000/Affirmations-App-Jetpack-Compose
rcallaby/Learn-Git
dwip708/EcoFusion
Badger0909/Hacktoberfest-2024
Anu27n/layout
suryanshsk/Python-Voice-Assistant-Suryanshsk
ianshulx/React-projects-for-beginners
mohamedammar27/Hacktoberfest-2k24
Mugunth-dev/Hacktoberfest2024
iamsamhhh/TravelPlanningApp
Syknapse/Contribute-To-This-Project
firstcontributions/first-contributions

Lessons from the Open Road

Contributing to open source code is less about proving your skills and more about collaborating with others to solve real problems.

Through these contributions, I noticed a clear progression in my development style. In the beginning, I worked on simple documentation edits and basic exercises. As I gained confidence, I moved on to larger issues such as system configuration scripts, security updates, and resolving compatibility bugs across operating systems.

What I learned while writing code for others:

Windows support is a hidden hurdle: Many developers use Unix-like systems, which means Windows-specific bugs slip through. I spent considerable time debugging paths for tools like cognee and test setups for openclaw to ensure Windows users have a smooth experience.
Clear communication speeds up review: A well-written pull request description with context, test instructions, and clean formatting saves maintainers a lot of time and gets merged faster.
Small steps lead to deep systems: I started with simple updates in first-contributions and eventually ended up working on advanced Graph RAG tools and AI agents.

⛬ My complete Contribution History

Here is the list of all my merged pull requests organized repository by repository. The repositories are ordered by my most recent contributions.

topoteretes/cognee

openclaw/openclaw

HSF/hsf.github.io

Consolidated GSoC 2026 website cleanups and formatting fixes

Tracer-Cloud/opensre

sumanth-0/100LinesOfPythonCode

TheAlgorithms/Python

Update README.md

brisbanesocialchess/awesome-social-chess

agnilondapakou/helloWorld

brisbanesocialchess/.github

bratergit/lastChanceHackertoberfest

Create automated-tests.yml

dipandhali2021/CineMagic

Update README.md

Abinash6000/Affirmations-App-Jetpack-Compose

Update README.md

rcallaby/Learn-Git

dwip708/EcoFusion

Badger0909/Hacktoberfest-2024

Anu27n/layout

suryanshsk/Python-Voice-Assistant-Suryanshsk

10 simple python algorithms Solutions

ianshulx/React-projects-for-beginners

Update index.html

mohamedammar27/Hacktoberfest-2k24

Create binary_search.cpp

Mugunth-dev/Hacktoberfest2024

iamsamhhh/TravelPlanningApp

Syknapse/Contribute-To-This-Project

Aniruddha Adak's Card

firstcontributions/first-contributions

Update Contributors.md

What Comes Next

Putting my work out in the public has been both challenging and incredibly rewarding. Every single merge represents a puzzle solved, a conversation with a maintainer, and a step forward in my engineering journey. I plan to keep building, experimenting with advanced AI tools, and sharing my code with the world.

If you want to talk about open source, Python, or AI systems, feel free to connect with me. Let us keep creating in the open.

The Code I Shared: Reflections on my best 29+ Merged Pull Requests in Open Source

ANIRUDDHA ADAK — Sun, 21 Jun 2026 07:37:47 +0000

Writing code in public changed the way I think about software development. When I merged my first pull request on New Year's Day of 2024, it felt like a small step. Over the course of thirty months and many merged contributions, that small step grew into a major part of my growth as a developer.

I want to share my curated contribution history. In this document, you will find my best pull requests merged across various repositories, starting from my most recent contributions to my very first one. This timeline shows my progress from practicing basic Python scripts to resolving Windows system bugs and working on advanced Graph RAG tools like cognee.

The Numbers and Projects

Here is a summary of the key external projects where I spent my time. Instead of working in isolation, I had the opportunity to build features and fix bugs for several active communities. All repository titles listed below are clickable links.

topoteretes/cognee: This is a core Graph RAG framework for LLM workflows. I worked on resolving critical pathing bugs for Windows systems and graph visualization engines.
google-gemini/gemini-cli: A command-line utility for interacting with the Google Gemini models.
NousResearch/hermes-agent: A project focused on building robust AI agent frameworks.
TheAlgorithms/Python: A community collection of standard algorithms implemented in Python.
sumanth-0/100LinesOfPythonCode: I contributed enhancements and exercises to help Python learners build practical projects.
brisbanesocialchess/awesome-social-chess: A project setting up configurations and awesome lists for a local social chess group.

What I Learned Along the Way

Contributing to open source code is less about proving your skills and more about collaborating with others to solve real problems.

One key lesson is that cross-platform support matters. When writing code, testing on multiple operating systems is essential. I spent significant time resolving Windows-specific file path issues for libraries like cognee and test suites for openclaw. These issues are often overlooked by developers working on Unix-based systems.

Another lesson is that clear communication is a superpower. Every repository has its own guidelines and style. Working within those boundaries and explaining my changes clearly helped maintainers review my code quickly.

Curated Contribution Log

Here is the list of my selected merged pull requests, showcasing a single key contribution per repository. They are ordered from my most recent changes in June 2026 back to my very first contribution in January 2024. All repository titles in the log are clickable links.

June 2026

topoteretes/cognee fix(lancedb): automatically prefix windows paths to resolve OS Error 3 for long paths (fixes #2941)

fix(lancedb): automatically prefix windows paths to resolve OS Error 3 for long paths (fixes #2941) #3123

aniruddhaadak80 posted on Jun 20, 2026

Closes #2941

This PR automatically normalizes and prefixes absolute Windows paths for the vector_db_url when using local filesystem storage. This resolves OS Error 3 triggered by LanceDB subprocesses when generating long file paths for persisting vector data on Windows.

View on GitHub
openclaw/openclaw test: make install-safe-path symlink tests compatible with Windows
test: make install-safe-path symlink tests compatible with Windows #90275
aniruddhaadak80 posted on Jun 04, 2026
Summary
- Run the existing install-path symlink boundary tests on Windows when directory junctions are supported.
- Use Windows junctions for directory links while preserving dir symlinks elsewhere.
- Keep production install-path behavior unchanged.
- Treat temporary-directory or cleanup failures in the capability probe as unsupported test environments instead of failing module import.
Linked context

No linked issue. This is a test portability improvement for existing install-path boundary coverage.

Real behavior proof
- Behavior addressed: Three install-safe-path symlink boundary tests were unconditionally skipped on Windows.
- Real environment tested: Native Windows Azure VM (Standard_D4ads_v6) through Crabbox.
- Exact steps or command run after this patch: node scripts/run-vitest.mjs src/infra/install-safe-path.test.ts
- Evidence after fix: Native Windows console output from Crabbox lease cbx_be4230e2069c, run run_0fb83e164185:
```
RUN  v4.1.8 C:/repo/openclaw

✓ infra src/infra/install-safe-path.test.ts (24 tests) 525ms

Test Files  1 passed (1)
Tests       24 passed (24)
```
- Observed result after fix: The directory-junction cases executed successfully on native Windows instead of being skipped by platform.
- What was not tested: No end-user install flow was exercised because the patch changes tests only.
- Proof limitations or environment constraints: The tests still skip when the host cannot create directory links.
- Before evidence: Current main uses it.runIf(process.platform !== "win32") for all three cases.
Tests and validation
- node scripts/run-vitest.mjs src/infra/install-safe-path.test.ts
- node scripts/run-oxlint.mjs src/infra/install-safe-path.test.ts
- Native Windows Crabbox: 24/24 tests passed
- Blacksmith Testbox tbx_01kv72nvfyz4fgpny8cyn48xfr: pnpm check:changed
- .agents/skills/autoreview/scripts/autoreview --mode branch --base origin/main
Risk checklist
- Did user-visible behavior change? No
- Did config, environment, or migration behavior change? No
- Did security, auth, secrets, network, or tool execution behavior change? No
- Highest-risk area: Windows directory-link capability detection in the test harness.
- Mitigation: Capability-gated execution plus direct native-Windows proof.
Current review state
- Next action: Refresh CI on the rebased head and merge when required checks pass.
- Addressed review comments: Temporary directory creation and cleanup are contained by the probe; module-level probing remains intentional because Vitest evaluates skipIf during test declaration.
View on GitHub

May 2026

HSF/hsf.github.io Consolidated GSoC 2026 website cleanups and formatting fixes
Consolidated GSoC 2026 website cleanups and formatting fixes #1908
aniruddhaadak80 posted on Mar 13, 2026
As discussed in #1907, I am compiling all of the minor fixes and metadata cleanups into this single, unified PR to drastically reduce review noise.

Changes Included:
1. GSoC 2026 Project Metadata: Appends the missing epository and license fields to the remaining 2026 project YAML headers (Clad, CMS, CernVM-FS, FCC, Key4hep, NNPDF, Spack) resolving #1862 cleanly.
2. GSoC Terminology: Replaces legacy instances of student with participant throughout the 2026 _gsocproposals/ files to match the current Google specs.
3. Template Validation Bug: Adds missing quotes around lt="{{ page.author }}" in _layouts/blog_post.html so it parses correctly for authors with multi-word names.
If any of these changes are not desired, just let me know and I can squash/drop them out of this PR immediately!

Closes #1907 Closes #1862
</div> <div class="gh-btn-container"><a class="gh-btn" href="https://github.com/HSF/hsf.github.io/pull/1908">View on GitHub</a></div>
April 2026
1. Tracer-Cloud/opensre fix: Database logic expansion for QA Edge Cases (Batch 2)
  
  fix: Database logic expansion for QA Edge Cases (Batch 2) #626
  
  aniruddhaadak80 posted on Apr 17, 2026
  
  _build_database_directive() has been expanded exponentially to train the AI to parse red herrings, distinguish between dual fault symptoms versus single root causes, infer missing Storage metrics organically, ignore healthy oscillating traffic metrics, and trace WAL replication lags adequately.
  
  View on GitHub
2. NousResearch/hermes-agent fix(permissions): handle None response from ACP request_permission
  fix(permissions): handle None response from ACP request_permission #13457
  aniruddhaadak80 posted on Apr 21, 2026
  
  What does this PR do?
  
  This PR hardens the ACP ? Hermes permission-approval bridge by safely handling an unexpected None result from equest_permission, preventing attribute errors and defaulting to a safe deny.
  
  Related Issue
  
  Fixes #13449
  
  Type of Change
  
  [x] ?? Bug fix (non-breaking change that fixes an issue)
  
  [ ] ? New feature (non-breaking change that adds functionality)
  
  [ ] ?? Security fix
  
  [ ] ?? Documentation update
  
  [x] ? Tests (adding or improving test coverage)
  
  [ ] ?? Refactor (no behavior change)
  
  [ ] ?? New skill (bundled or hub)
  
  Changes Made
  
  Return "deny" when equest_permission resolves to None in the approval callback.
  
  Add a unit test covering the None response case to ensure the callback denies safely.
  
  How to Test
  
  Connect via an ACP client that sends an empty response to permission requests.
  
  Verify the permission is denied rather than throwing an exception.
  
  Checklist
  
  Code
  
  [x] I've read the Contributing Guide
  
  [x] My commit messages follow Conventional Commits
  
  [x] I searched for existing PRs to make sure this isn't a duplicate
  
  [x] My PR contains only changes related to this fix/feature (no unrelated commits)
  
  [x] I've run pytest tests/ -q and all tests pass
  
  [x] I've added tests for my changes (required for bug fixes, strongly encouraged for features)
  
  [x] I've tested on my platform
  
  Documentation & Housekeeping
  
  [x] I've updated relevant documentation (README, docs/, docstrings) � or N/A
  
  [x] I've updated cli-config.yaml.example if I added/changed config keys � or N/A
  
  [x] I've updated CONTRIBUTING.md or AGENTS.md if I changed architecture or workflows � or N/A
  
  [x] I've considered cross-platform impact (Windows, macOS) per the compatibility guide � or N/A
  
  [x] I've updated tool descriptions/schemas if I changed tool behavior � or N/A
  
  </div> <div class="gh-btn-container"><a class="gh-btn" href="https://github.com/NousResearch/hermes-agent/pull/13457">View on GitHub</a></div>
  March 2026
  1. vgvassilev/clad docs: add documentation for non_differentiable attribute
    
    docs: add documentation for non_differentiable attribute #1773
    
    aniruddhaadak80 posted on Mar 13, 2026
    
    This PR resolves #1271 by officially documenting the non_differentiable attribute functionality within UsingClad.rst.
    
    It provides clear C++ examples of how to apply the annotation alias to both members and function declarations, illustrating how Clad handles each case.
    
    Closes #1271.
    
    View on GitHub
  2. accomplish-ai/coworker docs: add testing instructions to CONTRIBUTING.md
    
    docs: add testing instructions to CONTRIBUTING.md #575
    
    aniruddhaadak80 posted on Feb 21, 2026
    
    Description
    
    While CONTRIBUTING.md currently instructs developers to run pnpm build, pnpm typecheck, and pnpm lint, it does not provide any instructions on how to actually test the code before submitting a Pull Request.
    
    This PR copies the established testing commands from CLAUDE.md and creates a dedicated Testing section in CONTRIBUTING.md to improve the developer experience for new contributors.
    
    Testing
    
    This is purely a documentation change. The markdown successfully lints.
    
    Fixes # (No existing issue, proactively improving docs)
    
    Summary by CodeRabbit
    
    Documentation
    
    Added comprehensive Testing section to contribution guidelines with standardized test commands for Web UI, Desktop App, and Core Logic, including all-default and focused test scripts.
    
    View on GitHub
  3. google-gemini/gemini-cli feat(ui): remove write todo list tool from UI tips
    
    feat(ui): remove write todo list tool from UI tips #22281
    
    aniruddhaadak80 posted on Mar 13, 2026
    
    Fixes #22240
    
    View on GitHub
  4. GetStream/Vision-Agents docs: clarify Cartesia role in README (Fixes #268)
    
    docs: clarify Cartesia role in README (Fixes #268) #366
    
    aniruddhaadak80 posted on Feb 21, 2026
    
    Closes #268
    
    Corrected the description for Cartesia in the examples table to accurately reflect that Cartesia handles the emotional TTS alongside a vision model.
    
    cc: @aliev @nash0x7e2 @d3xvn
    
    Summary by CodeRabbit
    
    Documentation
    
    Updated Cartesia demo description to clarify how Sonic 3 model works alongside a vision model for storytelling capabilities.
    
    View on GitHub
  February 2026
  1. RocketChat/Rocket.Chat docs: update YouTube capitalization in README
    
    docs: update YouTube capitalization in README #38871
    
    aniruddhaadak80 posted on Feb 21, 2026
    
    Proposed changes
    
    Corrects the capitalization of "YouTube" in the README social media links to align with proper brand guidelines.
    
    Type of change
    
    [x] Documentation Update
    
    Summary by CodeRabbit
    
    Documentation
    
    Corrected capitalization of brand name in README.
    
    COMM-144
    
    View on GitHub
  October 2025
  1. sumanth-0/100LinesOfPythonCode Add Eco-Friendly Tip of the Day - Fixes #698
    
    Add Eco-Friendly Tip of the Day - Fixes #698 #716
    
    aniruddhaadak80 posted on Oct 11, 2025
    
    Description
    
    This PR implements a Python script that displays random eco-friendly tips to promote environmental awareness and sustainable living practices.
    
    Changes
    
    Created snippets/eco_friendly_tip_of_the_day/ folder
    
    Added eco_friendly_tip.py - Main Python script (84 lines)
    
    Added README.md - Comprehensive documentation
    
    Features
    
    ✅ 25 Unique Tips: Curated collection covering energy, water, waste, transportation, and more ✅ Interactive Menu: User-friendly CLI with options to display new tips or exit ✅ Beautiful Formatting: Clean output with emojis for better readability ✅ Under 100 Lines: Complete implementation in 84 lines of Python ✅ No Dependencies: Uses only Python standard library (random, sys) ✅ Well Documented: Comprehensive README with usage examples
    
    Testing
    
    The script has been tested and works as expected:
    
    Displays random tips from the curated list
    
    Interactive menu responds correctly to user input
    
    Clean exit on 'Q' command
    
    Shows new tip on 'N' command
    
    Checklist
    
    [x] Code is under 100 lines
    
    [x] Follows PEP 8 style guidelines
    
    [x] Includes README with usage instructions
    
    [x] References issue #698
    
    [x] No external dependencies
    
    [x] Proper folder structure in snippets/
    
    Issue
    
    Fixes #698
    
    Screenshots/Example Output
    
    🌿 Welcome to Eco-Friendly Tips! 🌿 Help save the planet, one tip at a time! ============================================================ 🌍 ECO-FRIENDLY TIP OF THE DAY 🌍 ============================================================ 💡 Switch to LED bulbs - they use 75% less energy than traditional bulbs! ============================================================ Options: [N] - Show another tip [Q] - Quit Your choice:
    
    Created as part of Hacktoberfest to promote environmental awareness! 🌱
    
    View on GitHub
  August 2025
  1. TheAlgorithms/Python Update README.md
    
    Update README.md #12345
    
    aniruddhaadak80 posted on Nov 01, 2024
    
    Add emojis for enhanced visual appeal and readability in README .
    
    Describe your change:
    
    Add emojis for enhanced visual appeal and readability in README
    
    [ ] Add an algorithm?
    
    [ ] Fix a bug or typo in an existing algorithm?
    
    [x] Add or change doctests? -- Note: Please avoid changing both code and tests in a single pull request.
    
    [ ] Documentation change?
    
    Checklist:
    
    [x] I have read CONTRIBUTING.md.
    
    [x] This pull request is all my own work -- I have not plagiarized.
    
    [x] I know that pull requests will not be merged if they fail the automated tests.
    
    [x] This PR only changes one algorithm file. To ease review, please open separate PRs for separate algorithms.
    
    [x] All new Python files are placed inside an existing directory.
    
    [x] All filenames are in all lowercase characters with no spaces or dashes.
    
    [x] All functions and variable names follow Python naming conventions.
    
    [x] All function parameters and return values are annotated with Python type hints.
    
    [x] All functions have doctests that pass the automated testing.
    
    [x] All new algorithms include at least one URL that points to Wikipedia or another similar explanation.
    
    [ ] If this pull request resolves one or more open issues then the description above includes the issue number(s) with a closing keyword: "Fixes #ISSUE-NUMBER".
    
    View on GitHub
  April 2025
  1. brisbanesocialchess/awesome-social-chess Create welcome.yml
    
    Create welcome.yml #90
    
    aniruddhaadak80 posted on Oct 29, 2024
    
    For issue #89
    
    View on GitHub
  January 2025
  1. agnilondapakou/helloWorld Update script.js and README.md for Transact-SQL
    
    Update script.js and README.md for Transact-SQL #447
    
    aniruddhaadak80 posted on Jan 19, 2025
    
    View on GitHub
  October 2024
  1. brisbanesocialchess/.github Create proselint.yml
    
    Create proselint.yml #63
    
    aniruddhaadak80 posted on Oct 29, 2024
    
    For issue #46
    
    View on GitHub
  2. bratergit/lastChanceHackertoberfest Create automated-tests.yml
    
    Create automated-tests.yml #22
    
    aniruddhaadak80 posted on Oct 29, 2024
    
    For issue #6
    
    View on GitHub
  3. dipandhali2021/CineMagic Update README.md
    
    Update README.md #17
    
    aniruddhaadak80 posted on Oct 29, 2024
    
    Add Firebase Configuration for issue #16
    
    View on GitHub
  4. Abinash6000/Affirmations-App-Jetpack-Compose Update README.md
    
    Update README.md #33
    
    aniruddhaadak80 posted on Oct 29, 2024
    
    For issue #32
    
    View on GitHub
  5. rcallaby/Learn-Git Create بنیادی-نیویگیشن.md
    
    Create بنیادی-نیویگیشن.md #759
    
    aniruddhaadak80 posted on Oct 27, 2024
    
    for issue #680
    
    View on GitHub
  6. dwip708/EcoFusion Update ContactUs.js
    
    Update ContactUs.js #18
    
    aniruddhaadak80 posted on Oct 27, 2024
    
    Enhanced the contact page with responsive layout, modern styling (gradient background, shadows, animations), proper spacing, and improved navigation while maintaining all content in a single viewport using Tailwind CSS utility classes.
    
    For issue #12
    
    View on GitHub
  7. Badger0909/Hacktoberfest-2024 Create ToDoList.java
    
    Create ToDoList.java #28
    
    aniruddhaadak80 posted on Oct 25, 2024
    
    A command-line To-Do List application in Java that allows users to add, remove, and display tasks without a graphical interface. #25
    
    View on GitHub
  8. Anu27n/layout Update Card.js
    
    Update Card.js #8
    
    aniruddhaadak80 posted on Oct 25, 2024
    
    Summary of Code Changes :
    
    Added round corners and changed colors for a modern look.
    
    Included hover effects that scale or change colors.
    
    Improved button styles to make them stand out.
    
    </div> <div class="gh-btn-container"><a class="gh-btn" href="https://github.com/Anu27n/layout/pull/8">View on GitHub</a></div>
  9. suryanshsk/Python-Voice-Assistant-Suryanshsk 10 simple python algorithms Solutions
    
    10 simple python algorithms Solutions #439
    
    aniruddhaadak80 posted on Oct 23, 2024
    
    Implemented algorithms for GCD, LCM, Sieve of Eratosthenes, Exponentiation by Squaring, Binary Exponentiation, Flood Fill, Two Pointer Technique, Sliding Window, Kadane's Algorithm, and Longest Common Subsequence, all with user input support.
    
    View on GitHub
  10. ianshulx/React-projects-for-beginners Update index.html
    
    Update index.html #119
    
    aniruddhaadak80 posted on Oct 23, 2024
    
    View on GitHub
  11. mohamedammar27/Hacktoberfest-2k24 Create binary_search.cpp
    
    Create binary_search.cpp #6
    
    aniruddhaadak80 posted on Oct 22, 2024
    
    Implement binary search algorithm in C++ for efficient searching in sorted arrays.
    
    View on GitHub
  12. Mugunth-dev/Hacktoberfest2024 Create palindrome-checker.py
    
    Create palindrome-checker.py #11
    
    aniruddhaadak80 posted on Oct 22, 2024
    
    View on GitHub
  13. iamsamhhh/TravelPlanningApp Create CreateTravelPlan.py
    
    Create CreateTravelPlan.py #11
    
    aniruddhaadak80 posted on Oct 22, 2024
    
    View on GitHub

How to Actually Set Up Claude Projects That Most Users Don't Know

ANIRUDDHA ADAK — Thu, 18 Jun 2026 04:34:00 +0000

Most people treat Claude Projects like a folder with a label on it.

They create a project. They give it a name. Maybe they write a sentence or two in the system prompt. Maybe they upload a file. Then they start chatting and wonder why the output feels the same as regular Claude.

That is not a project. That is a labeled chatbox.

The people getting insane results from Claude Projects are building something completely different. They are building customized AI employees. Each project is tuned so precisely that Claude knows their voice, their standards, their audience, their workflow, and their exact definition of "good output" before they type a single word.

I spent three weeks testing every possible project configuration. Different system prompts. Different knowledge file combinations. Different organizational structures. I rebuilt the same project twelve times until the output was consistently excellent with minimal prompting.

Here is the exact blueprint that works.

Why Most Claude Projects Fail

There are three reasons most people's projects produce mediocre output.

Reason 1: The system prompt is too vague.

"You are a helpful writing assistant" is not a system prompt. It is a wasted opportunity. Claude already knows how to be helpful. What it does not know is your specific voice, your audience, your formatting rules, your quality bar, and the fifteen things you hate seeing in an output.

Reason 2: There are no knowledge files uploaded.

A project without knowledge files is just a labeled conversation. The entire point of a project is that Claude has persistent access to reference documents that shape every single response. If you are not uploading files, you are not using the feature.

Reason 3: The project tries to do everything.

One project called "Work Stuff" that handles emails, content, strategy, research, coding, and analysis will produce mediocre results across all of them. Separate your workflows into separate projects. Each one should do one thing extremely well.

The 6-Part Project Blueprint

Every high-performing project has six components. Skip any of them and the output quality drops noticeably. I tested this by removing each component one at a time and measuring the difference in output quality. The drop was immediate every single time.

Part 1: The Identity Block

This is the first thing in your system prompt. It tells Claude exactly who it is in this project. Not "you are a helpful assistant." Something like this:

You are my senior content strategist and writer. You have been working with me for two years. You know my voice, my audience, and my quality standards intimately.
Your job in this project is to research, plan, draft, and edit long-form articles for my audience of 250,000 followers who are learning AI tools for the first time.
You are direct. You are specific. You never use filler. Every sentence earns its place or gets cut.

Why this works: Claude performs dramatically better when it has a clear identity. "You have been working with me for two years" creates a framing where Claude defaults to confident, specific output rather than generic safe output. The specificity of "250,000 followers who are learning AI tools" gives it a concrete audience to write for.

Part 2: The Rules Block

This is the non-negotiable list of things Claude must always do and must never do in this project. This is the most important part of your system prompt by far.

ALWAYS:

Use short paragraphs. Maximum 3 sentences per paragraph.

Include specific numbers rather than vague claims. Never say "many" or "some." Give a number.

Bold the key insight in every section so a skimmer can read just the bold text and get the core message.

Open with a hook that creates a gap between what the reader thinks they know and what is actually true.

End with a CTA that creates urgency without being salesy.

NEVER:

Use the word "leverage" or "utilize" or "streamline"

Start any sentence with "In today's" or "It's important to note"

Use em dashes

Write generic advice that could apply to anyone. Every point must be specific and actionable.

Include disclaimers or hedging language like "it depends" without immediately following with the specific thing it depends on

Why this works: Rules are constraints. Constraints force Claude into a specific lane where it cannot default to its generic patterns. Every time you find yourself correcting Claude's output, that correction should become a rule. After a month of this, your rules list is a precision instrument.

Part 3: The Process Block

This tells Claude exactly how to approach work in this project. Not what to produce but how to think.

PROCESS:

Before writing anything, spend your first paragraph thinking about what the reader already believes about this topic. Your opening must challenge that belief.

Outline the complete structure before writing any section. Share the outline with me for approval before proceeding.

Write the first draft in one pass without self-editing.

Review the draft against my Rules. Fix any violations.

Check every claim for a specific supporting number or example. Replace anything vague.

Read the entire piece as if you are a busy person scrolling on their phone. Cut anything that would make them stop reading.

Why this works: Without a process block, Claude jumps straight to generating output. With one, it follows a structured workflow that produces consistently higher-quality results. The "read as a busy person scrolling on their phone" instruction alone improved my content quality more than any other single line.

Part 4: The Output Format Block

This defines exactly what the finished product looks like. Eliminate all guessing.

OUTPUT FORMAT:

Title: [Number] + [Claude-Specific Topic] + [Exclusion Hook]

Length: 2000-3500 words

Structure: Bold contrast opener (2 paragraphs) > Context section > Numbered main content > Strong closing CTA

Subheadings: Bold, written as hooks that make the reader want to read the section

Closing: Italic line + bold italic line using the "most people will X / the ones who Y" dichotomy

Why this works: When Claude knows the exact format before it starts writing, it distributes its effort correctly. Without this, it front-loads detail in the early sections and rushes the ending. With it, every section gets proportional attention.

Part 5: The Knowledge Files

This is where your project gets its real intelligence. Upload these files and Claude references them in every single conversation:

File 1: Style guide (REQUIRED) A document showing your writing style. Include at least three examples of your best work. Claude pattern-matches against these examples to capture your voice. Without this file, Claude writes in its default style. With it, Claude writes in yours.
File 2: Audience profile (REQUIRED) Who reads your content? What do they already know? What frustrates them? What are they trying to achieve? What level of technical depth do they want? Write this as a one-page persona document. Claude uses it to calibrate every piece of content.
File 3: Competitor analysis (RECOMMENDED) What does the competition look like in your space? What topics have been covered to death? What angles are overused? Upload examples of content you want to be different from. Claude uses this to avoid generic takes.
File 4: Performance data (RECOMMENDED) Which of your past pieces performed best? What titles got the most engagement? What topics resonated? Upload your metrics. Claude uses this data to make better strategic decisions about what to write and how to position it.
File 5: Template library (RECOMMENDED) Your best-performing formats as reusable templates. Article structures. Email frameworks. Report layouts. Claude uses these as starting frameworks instead of inventing structure from scratch every time.

Part 6: The Onboarding Message

This is the first message you send in a new conversation within the project. Think of it as the daily briefing you would give an employee.

Today we are working on [TASK].
Here is the context: [WHAT I NEED AND WHY]
Reference my style guide for voice. Reference my audience profile for targeting. Reference my performance data for strategic decisions.
Before starting, tell me:

What angle would make this stand out from what already exists?

What does the reader already believe that we can challenge?

What is the one-sentence hook?

Once I approve the angle, proceed with a full outline.

Why this works: This message activates all five knowledge files, sets the task context, and forces Claude to think strategically before writing. Without it, Claude starts generating immediately. With it, Claude plans first and executes better.

The Complete System Prompt Template (Copy This)

Here is the full system prompt, assembled from all six parts. Copy it. Modify the details for your specific use case. Paste it into your project.

IDENTITY:
You are my [ROLE]. You have worked with me for two years. You know my voice, my audience, and my standards.

Your job in this project is to [PRIMARY FUNCTION] for [AUDIENCE DESCRIPTION].

You are [COMMUNICATION STYLE]. You never [ANTI-PATTERNS].

RULES:
ALWAYS:
- [Rule 1]
- [Rule 2]
- [Rule 3]
- [Rule 4]
- [Rule 5]

NEVER:
- [Anti-rule 1]
- [Anti-rule 2]
- [Anti-rule 3]
- [Anti-rule 4]

PROCESS:
1. [Step 1 - Think/Plan]
2. [Step 2 - Outline/Structure]
3. [Step 3 - Execute]
4. [Step 4 - Review against rules]
5. [Step 5 - Quality check]

OUTPUT FORMAT:
- Format: [EXACT FORMAT SPECIFICATION]
- Length: [WORD COUNT RANGE]
- Structure: [SECTION ORDER]
- Style: [SPECIFIC STYLE MARKERS]

The 5 Projects Everyone Needs

You do not need twenty projects. You need five. Each one handles one core workflow.

Project 1: Content Production System prompt defines your writing voice, audience, and content standards. Knowledge files include your style guide, best-performing pieces, audience persona, and content calendar.
Project 2: Research and Analysis System prompt defines how you want research structured, what sources you trust, and what depth of analysis you expect. Knowledge files include your research methodology, past analyses as examples, and your evaluation criteria.
Project 3: Communication System prompt defines your email and messaging style, your professional tone, and your relationship with different audiences (clients, team, leadership). Knowledge files include examples of great emails you have written and templates for common message types.
Project 4: Strategy and Planning System prompt defines your business context, your goals, your constraints, and how you think about decisions. Knowledge files include your business plan, competitor data, market research, and strategic frameworks you use.
Project 5: Code and Technical Work System prompt defines your tech stack, coding conventions, architecture preferences, and quality standards. Knowledge files include your CLAUDE.md content, architecture decision records, and code examples that show your preferred patterns.

What Happens After You Set This Up

Here is what changes.

Before this setup: You type a detailed prompt. Claude gives you a decent response. You spend twenty minutes editing it to match your voice and standards. You do this five to ten times a day.
After this setup: You type a two-sentence task description. Claude gives you an output that already matches your voice, follows your rules, targets your audience, and hits your quality bar. You spend two minutes reviewing it. You do this five to ten times a day.

That is a 90% reduction in editing time across every single task you delegate to Claude.

The math is simple. If you use Claude for two hours a day and this saves you 60% of your editing and correction time, that is over an hour saved daily. Five hours per week. Over 250 hours per year.

That is six full work weeks of your life back. From a one-time setup that takes about forty-five minutes.

Most people will keep using Claude Projects as labeled chat windows and wonder why their results are average.

The ones who build their blueprint today will never prompt the old way again. And they will never understand how they worked without it.

hope this was useful for you ❤️

The Ultimate AI & AI Agents Directory: 50+ Tools Every Developer Should Know

ANIRUDDHA ADAK — Mon, 15 Jun 2026 10:24:00 +0000

The AI landscape is evolving faster than ever. Keeping track of the right tools can feel like trying to drink from a firehose.

Whether you're looking for an autonomous coding agent, a rapid app builder, an LLM benchmark, or a creative suite, you need the right tool for the job.

I've combed through the latest and greatest AI tools and AI Agents to bring you this mega-compilation. Bookmark this page, because you're going to want to refer back to it.

1. The Coding Companions & App Builders

These AI agents and platforms will write, deploy, and ship code for you at lightspeed.

Devin: The autonomous AI Software Engineer that can plan, build, and deploy entire applications.
v0 by Vercel: Generate beautiful UI components and frontend code seamlessly using natural language.
Bolt.new: Instant, in-browser web development environments powered by AI.
Replit: Build apps and sites with powerful AI assistance right in your IDE.
Kilo Code: An open-source AI agent extension specifically built for VS Code.
Kiro.dev: The AI IDE tailored for taking your projects from prototype to production.
Rocket.new: Build Web & Mobile apps 10x faster without writing a single line of code.
a0.dev: Create robust mobile apps rapidly with AI assistance.
Dora: Ship 3D animated websites without code, driven by AI.
Lovable: Craft software effortlessly through conversational interfaces.
Kineto by JetBrains: A no-code platform to generate websites and web apps with AI.
Jules: Google's advanced coding agent task platform.

2. LLMs, Chatbots, and Hubs

The core engines and interfaces for conversing with and utilizing frontier models.

Google Gemini: Google's flagship multimodal model, excelling at reasoning, coding, and creative tasks.
Le Chat by Mistral: Mistral's fast, capable, and highly efficient chat interface.
OpenRouter: The ultimate unified API for routing requests across dozens of LLMs seamlessly.
LM Studio: Run open-source models locally and entirely offline on your machine.
Poe: Fast, helpful AI chat giving you access to a variety of top-tier models in one place.
Kimi AI: A powerful chatbot known for handling massive context windows and deep understanding.
Okara.ai: Chat privately with open-source models.
Google AI Studio: The best prototyping environment for Google's latest generative models and APIs.

3. Benchmarks & Analytics

How do you know which AI is actually the best? These platforms test and measure model intelligence.

LMArena: The gold standard for crowdsourced, blind LLM benchmarking.
SWE-bench: Leaderboards testing AI models on real-world, complex software engineering issues.
CodeArena: Pitting LLMs against each other to determine which one codes the best.
Artificial Analysis: Deep dives, metrics, and comparisons of AI model and API provider performance.

4. Creative Suites: Images, UI, & Video

Unleash your inner artist and designer with these visual powerhouses.

ImagineArt: Comprehensive AI creative suite covering images, videos, and voice.
Krea: Real-time AI image generation and high-quality upscaling.
Freepik AI: An all-in-one AI creative suite backed by Freepik's massive asset library.
Wan AI: A leading AI video generation model pushing the boundaries of motion synthesis.
Hedra: Powerful AI character and video generation.
Lovart: The world’s first autonomous design agent.
Flova.ai: AI video creation agent platform built specifically for creators and storytellers.
Magic Patterns: AI-generated UI components for rapid prototyping.

5. Voice, Music, & Audio Agents

The next frontier of AI isn't just text—it's audio.

AssemblyAI: Top-tier, production-ready AI models to transcribe and understand speech.
Hume AI: Empathic Voice Interface that understands vocal intonation and emotional expression.
MusicGPT: Instant AI music generation right from your browser.
Producer.ai: An autonomous AI music agent for full track production.
Typeless: AI voice dictation that is actually intelligent and understands context.
Vogent: Create and deploy custom AI voice agents.

🛠 6. Developer Tools, Search & Productivity

Supercharge your workflow, scrape the web, and build a second brain.

Firecrawl: Turn any website into clean markdown or structured data, perfect for feeding to LLMs.
DeepWiki: AI documentation that you can talk to, available for every repository.
Supermemory: Build a second brain and effortlessly recall everything you consume online.
Cora: Give Cora your inbox, and take back your life. An AI agent for email management.
PromptBase: The ultimate prompt marketplace for Midjourney, ChatGPT, FLUX, and more.
Chat4Data: An AI-powered web scraping extension.
Flowith: Your AI Creation Workspace, infused with knowledge capabilities.
There's An AI For That: The standard directory for browsing AI tools for literally any task.

Wrapping Up

The era of AI Agents is just beginning. Whether you are scaffolding a full-stack application with Devin or v0, analyzing data with Firecrawl, or exploring new models on LM Studio, these tools are designed to 10x your productivity.

Which AI tools are part of your daily stack?
Did I miss any of your favorites?
Let me know in the comments below. 👇

Claude Can Do All of This. Most People Have No Idea.

ANIRUDDHA ADAK — Thu, 11 Jun 2026 13:43:00 +0000

Claude can do a lot more than most people think.

This guide covers all of it—where to find each feature, how to turn it on, and a prompt you can use immediately. Go through it, pick what fits your workflow, and set it up today. Each one takes minutes to set up and pays off every single day after that.

Hidden Features in Regular Claude

1. Projects - Claude that actually remembers you

Every time you open a new Claude chat, it starts from zero. It doesn't know your name, your work, your preferences—nothing. Most people accept this and re-explain themselves every single conversation.

Projects fix this. You create a project, upload your documents, write standing instructions—and Claude holds all of it permanently. Open it next week and it picks up exactly where you left off.

Note: If you've never used Projects, this is the first thing to fix before anything else in this article.

Example project instructions (paste into the Project Instructions field):

You are my content research assistant. I run a newsletter about AI and crypto for a technical audience.

Always assume my readers know the basics. Don't explain what an LLM is or what a blockchain is.

When I share a topic or article, your job is to:
1. Identify the 3 most counterintuitive or surprising angles
2. Find connections to recent events I might have missed
3. Suggest how I could frame this as a story, not a summary

Tone: direct, no corporate language, no filler phrases.
Format: short paragraphs, no bullet points unless I ask.
Never start a response with "Great question" or "Certainly".

2. Artifacts - working apps inside your chat

Many people think Claude can only produce text. It can't build anything real. That's wrong.

Artifacts are when Claude builds something that actually works inside the chat. Not a block of code you have to copy somewhere—a live product in a side panel. A calculator, a habit tracker, a game, a dashboard with charts. You open it, click it, use it. Without leaving the conversation.

SVG graphics, interactive charts, Mermaid diagrams—all supported. Available on the free plan. Most people have never tried it.

Try this prompt:

Build me a habit tracker as a working web app.

I want to track 5 daily habits. 
Each day I can check them off. 
Show a 7-day streak counter for each habit. 
If I miss a day, the streak resets.

Design: dark background, clean minimal look. 
Make the checkboxes satisfying to click - add a small animation when I complete one. 
The data should persist if I refresh the page.

3. Adaptive Thinking - a different level of reasoning

Most Claude users have never turned this on. Extended Thinking is a mode where Claude reasons through a problem step by step before giving you an answer—and you can watch the entire process.

For simple questions, you don't need it. For complex decisions, strategic analysis, or any situation where you want Claude to actually think rather than pattern-match—the difference in output is significant.

Turn it on. Ask the same question you've been asking. Compare the answers.

Use this when facing a real decision:

I'm deciding between two options and I want you to think through this carefully before answering.

Option A: [describe option A]
Option B: [describe option B]

My situation: [your context, constraints, what matters most]

Work through this before responding. Think about:
- The second and third-order consequences of each option
- What I'm probably overweighting or underweighting emotionally
- What information I might be missing that would change the decision
- Which option has better downside protection if things go wrong

Then give me your actual recommendation with your reasoning.

4. Memory - Claude that knows who you are

With Memory on, Claude builds a profile of you over time. Your job, your projects, how you like to communicate, what you're currently working on.

Start a completely new chat and it already knows the context. You never introduce yourself again. It's off by default, and most people don't know it exists.

Try this prompt:

I want you to remember the following about me so you don't need to ask again:

My name is [name]. I work as [role] at [company or project].
My main focus right now is [what you're working on].
My audience or customers are [who they are].

When I ask for help, always assume this context unless I say otherwise.
My preferred communication style: [direct / detailed / casual / formal].
Things I find annoying in responses: [e.g. bullet points, long intros, excessive caveats].

Save all of this to memory now.

Give Claude a Role - One Prompt Changes Everything

Claude doesn't have to be "an AI assistant." Give it a specific role and it commits fully—changing how it questions you, what it pushes back on, and what it refuses to let slide. Copy any prompt below and paste it at the start of a new chat.

5. Personal Psychologist

Most people use Claude as a validation machine. They describe a problem. Claude says that sounds hard and offers five bullet points of advice. That's not how good therapy works.

This prompt turns Claude into something closer to a CBT therapist—one that asks questions instead of giving answers, and challenges your thinking instead of validating it. Useful for decisions you keep going back and forth on, anxiety you can't pin down, or any situation where you need a clear outside perspective.

You are a cognitive behavioral therapist with 20 years of experience. I'm going to share something I'm struggling with.

Your approach:
- Don't give advice immediately. Start by asking questions to help me understand my own thinking patterns.
- Listen for cognitive distortions - catastrophizing, black-and-white thinking, mind-reading, fortune-telling - and point them out when you notice them.
- Ask one question at a time. Don't overwhelm me.
- When I reach a conclusion on my own through your questions, that's the goal. Don't hand me the answer.
- Be warm but honest. Don't validate me if my thinking is clearly distorted.
- If I seem to be avoiding something important, name it directly.

Don't start with a clinical introduction. Just ask me what's going on.

6. The Hard Mentor

By default, Claude agrees with you. It adds to your ideas, supports your reasoning, finds the positives. This is almost always the wrong thing.

This prompt disables that. Claude stops validating and starts stress-testing. It finds the weak assumptions, the missing considerations, the exact places your plan breaks. It's uncomfortable. That's why it works.

You are a brutally honest mentor. You've built and failed at multiple companies. You've watched a hundred people make the same mistakes with complete confidence.

Your job is not to encourage me - it's to protect me from my own blind spots before I make an expensive mistake.

Rules:
- Disagree with me when you think I'm wrong. Be specific about why.
- Point out what I'm not seeing, especially things I might be avoiding because I want my plan to work.
- Ask hard questions I haven't thought to ask myself.
- If something is a bad idea, say it's a bad idea. Don't balance it with "on the other hand..."
- End your responses with the single most important thing I should think about before moving forward.

I'm about to share an idea. Do not be kind about it.

7. Personal Trainer

Generic fitness advice is everywhere. It doesn't account for your schedule, your injuries, your equipment, or your actual goals. Give Claude your real numbers and it builds a real plan. Not a template. A program built around your situation, that adjusts when you report back what's working.

You are an expert personal trainer and sports nutritionist. I want you to build me a complete training program.

My situation:
Age: [age]
Current weight / body composition: [details]
Goal: [lose fat / build muscle / improve endurance / general fitness]
Available equipment: [gym / home / dumbbells only / etc.]
Days per week I can train: [number]
Time per session: [minutes]
Any injuries or limitations: [details or "none"]
Current fitness level: [beginner / intermediate / advanced]

Build me a 12-week program. Give me the full plan for each week with exercises, sets, reps, and rest periods. Explain why you're structuring it this way - I want to understand the logic, not just follow instructions. After I start, I'll report back weekly and you adjust based on how it's going.

8. Practice a Difficult Conversation

Most people walk into hard conversations unprepared. They know what they want to say but not what the other person will actually say back.

Claude plays the other person. Realistically. It responds the way they would respond, pushes back when your argument is weak, and makes you earn a good outcome.

I need to prepare for a difficult conversation. I want you to roleplay as the other person so I can practice.

The person: [describe who they are - boss, client, co-founder, etc.]
What I need to say: [what you need to ask or tell them]
Why it's hard: [what you're afraid of / what could go wrong]
What this person is like: [their personality, how they typically react, what they care about]

Stay in character throughout the conversation. Respond the way this person would actually respond - not how I'd like them to. If I say something weak or unconvincing, push back on it.

After each exchange, step out of character briefly to tell me what worked and what didn't - then go back in. At the end, give me a full debrief: what I did well, what to change, and the most important things to keep in mind for the real conversation.

Start in character. Wait for me to open.

9. Devil's Advocate

You've made up your mind. You've already thought through the objections. You're convinced. That's exactly the moment to have Claude attack the decision. Not polite concerns—the full case against it.

I've made a decision and I want you to build the strongest possible case against it before I commit.

The decision: [describe exactly what you're planning to do]
My reasoning: [why you think it's a good idea]
What I've already considered: [objections you've already thought about]

Your job:
- Build the strongest possible case AGAINST this decision.
- Don't balance it with positives. I already believe in it - I need the counterargument.
- Find the assumptions I'm making that could be wrong.
- Describe the 3 most realistic ways this fails or backfires.
- Tell me what I'm probably underestimating.
- Tell me what I would need to believe for this to be a genuinely bad idea.

Be ruthless. If this is a mistake, I need to know now.

Product Features Most People Don't Know Exist

10. Claude in Chrome - Claude that sees what you see

Most people use Claude in a separate tab and manually copy-paste what they need. That's the hard way.

Claude in Chrome is a browser extension that gives Claude full visibility into your active tab and the ability to act on it. It reads the page, clicks links, fills forms, navigates to new URLs. You describe the task in plain English and step away.

Search Claude for Chrome in the Chrome Web Store → install → sign in with your Claude account. Click the extension icon to open the sidebar.

Example task to give it:

I'm on this job listings page. 

Go through every listing visible and extract: job title, company name, salary range if shown, and the top 3 requirements. 

Build me a comparison table sorted by salary, highest first. 

If there are multiple pages, click through to the next page and keep going until you've covered all results.

11. Claude Cowork - Claude that lives on your desktop

Claude on the web has no access to your computer. It can't see your files. You have to paste everything manually.

Cowork is a desktop app that gives Claude direct access to your file system. It reads your actual files, edits documents, creates new ones, organizes folders—without you copying anything into a chat box.

12. Scheduled Tasks - Claude that works while you sleep

Most people treat Claude as something they have to activate. Open a chat, type a request, wait for output, close the tab.

Scheduled Tasks change that. You set a task once and Claude executes it automatically at the time and frequency you choose. Every morning. Every Monday. Every hour. Claude runs it and saves the output to your folder.

Example scheduled task description:

Every weekday morning at 7:30am, do the following:
1. Search for the top AI and crypto news from the last 24 hours
2. Pick the 5 most important stories - focus on things that are surprising, counterintuitive, or have real implications for builders and investors
3. For each story write: headline, 2-sentence summary, why it matters
4. Save the result as "brief-[date].md" in my /briefs folder

Keep the tone direct and analytical. No fluff. Readable in 3 minutes.

13. Skills in Cowork - Install new capabilities like plugins

Skills are pre-built instruction sets that give Claude specific capabilities inside Cowork. Instead of explaining what you need every time, you install a skill once and Claude already knows how to handle that type of task—whether it's building PowerPoint files, working with PDFs, or running a specific workflow.

Think of it like apps on a phone. The base phone works without them. But with the right apps installed, it does a lot more.

How to find and install: Cowork → Customize → Skills. Click Browse plugins in the left sidebar to add new capabilities.

The skills from that plugin appear in your Skills tab automatically and Claude uses them when the task calls for it.

14. CLAUDE.md - Rules Claude reads automatically

In Cowork and Claude Code, you can create a CLAUDE.md file in your project folder. Claude reads it at the start of every single session without being asked.

Your coding conventions, writing style rules, or brand voice guidelines are followed instantly.

Write it once. Claude follows it across every session in that project forever.

Example CLAUDE.md file:

# Project: AI Newsletter

## About this project
Weekly newsletter about AI and crypto for builders and investors. 35,000 subscribers. Tone is direct, analytical, occasionally irreverent.

## Writing rules
- Short paragraphs. Max 3 sentences.
- No bullet points in editorial content. Prose only.
- No em dashes. Use hyphens or restructure the sentence.
- Numbers beat adjectives. Write "saves 3 hours" not "saves significant time".
- Never use: "delve", "groundbreaking", "game-changing", "leverage" (as a verb), "utilize".
- Contractions are fine and encouraged.

## Content rules
- Assume the reader knows what an LLM is. Don't explain basics.
- Lead with the most surprising or counterintuitive thing.
- Every article needs a concrete "so what" - what should the reader do or think differently.

## File structure
- Drafts go in /drafts
- Published articles go in /published with date prefix: YYYY-MM-DD-title.md
- Research notes go in /research

15. Claude Code - AI that writes, tests, and fixes code in your terminal

Some people still don't know you can write code with Claude. Claude Code takes that one step further. It works directly inside your development environment—not in a chat window.

It reads your actual codebase, writes new code, runs tests, reads the error messages, and fixes the bugs in a loop until the task is done. It integrates with VS Code and JetBrains, and can even be dropped into GitHub Actions to review or write pull requests.

16. Claude Design - AI for visual work

Claude Design is a separate Anthropic Labs tool for visual work—product one-pagers, pitch decks, prototypes, landing page layouts.

You describe what you need. Claude builds it and exports to PPTX, Canva, PDF, or HTML. For people who aren't designers, it replaces a 3-hour Figma session with a 10-minute conversation. Access it directly at claude.ai/design.

17. Prompt Caching - 90% cost reduction on API calls (DEV)

For developers building on the Claude API: if your requests include a large repeated context block, you're paying to re-process those same tokens on every single call.

Prompt Caching stores that content server-side. Subsequent calls reuse the cache instead of re-processing it. Up to 90% cost reduction on cached tokens and noticeably faster responses.

Add "cache_control": {"type": "ephemeral"} to the content block you want cached. Works for system prompts, large documents, and tool definitions.

{
  "model": "claude-opus-4-6",
  "system": [
    {
      "type": "text",
      "text": "[your large system prompt or reference document]",
      "cache_control": {"type": "ephemeral"}
    }
  ],
  "messages": [
    {
      "role": "user",
      "content": "[user message - this changes each call]"
    }
  ]
}

Note: The system prompt gets cached after the first call. Every subsequent call within 5 minutes reuses the cache.

You now know more about Claude than most people who use it every day.

Pick one feature from this list. Just one. Set it up today. You don't need to implement everything at once , knowing what exists is already half the battle.

Come back to this article when you're ready for the next one.

The Ultimate Developer's Directory: 282+ AI Tools & Agents You Need to Try

ANIRUDDHA ADAK — Mon, 08 Jun 2026 09:50:47 +0000

The AI landscape is evolving faster than ever. Keeping track of the right tools can feel like trying to drink from a firehose.

I recently dug through my extensive bookmarks folders and compiled every single AI tool and Autonomous Agent I've saved.

Whether you're looking for an autonomous coding agent, a rapid app builder, an LLM benchmark, or a creative suite, you need the right tool for the job. Bookmark this page, because you're going to want to refer back to it.

Wrapping Up

The era of AI Agents is just beginning. Whether you are scaffolding a full-stack application, analyzing data, or exploring new models, these tools are designed to 10x your productivity.

Which AI tools are part of your daily stack? Did I miss any of your favorites? Let me know in the comments below! 👇

Chrono Shift: Time Weaver - A Time-Bending Platformer Built with AI

ANIRUDDHA ADAK — Sat, 06 Jun 2026 03:44:07 +0000

hello everyone,

it's exciting to share chrono shift: time weaver with you. this is a game where you get to play with time itself. it's a puzzle platformer that asks you to bend time to solve challenges and get past tricky spots in the environment.

the concept

imagine seeing the same place in two different times, all at once. in chrono shift, you don't just go through a level once. you experience it twice, moving between the past and the present. this lets you build paths that wouldn't be there in just one timeline. maybe a bridge that's broken now was fine in the past. or a door that's locked today was open back then. by cleverly moving through these different times, you open up new ways forward. it's all about how well you can master time.

what makes it special

this game brings some really cool ideas to life. you can switch between past and present with just a button press, watching the world change around you. there are also ten unique levels, each one introducing new mechanics and challenges that help you get better at weaving time. the game features vibrant, hand-crafted pixel art with backgrounds that scroll at different speeds, making each era feel alive. you can also collect hidden time crystals in every level to unlock new challenges and achievements. the controls are super responsive, giving you smooth platforming with jumps, dashes, and time-shifts that feel just right. and don't forget the ambient soundtrack, which changes with your timeline shifts, pulling you deeper into the experience. it's even mobile-friendly, so you can weave time on any device with easy touch controls.

the journey

this game started from a simple thought: what if platformers could help us see things differently? by making you look at the same space from two different times, chrono shift becomes more than just a game. it's a little bit like thinking about how our choices in the past shape our present, and how understanding both can show us possibilities we never saw before.

play it here: Play now 🎮

video demo

while there isn't a video recording just yet, i really hope you'll try chrono shift for yourself. the game has smooth animations that show the time-shift mechanic in action, with the world transforming between past and present. you'll see platforms appear, obstacles vanish, and new paths open up through this time magic. the pixel art truly comes alive with backgrounds that shift as the timeline changes, and the music adapts to wherever you are in time.

see the game project: full projects

code

this game was built entirely with the help of lovable, an ai coding assistant. the code behind it includes things like html5 canvas for drawing the game world, a custom game engine that handles physics, collisions, and switching timelines, a particle system for cool time-shift effects, and localstorage to save your progress and collected crystals. it also has a responsive design that works well with both keyboards and touch controls. the whole game was made just by talking to the ai, showing how artificial intelligence can really speed up game development while still letting you be creative.

how i built it

chrono shift: time weaver is a fresh step in game development, where human creativity gets a boost from ai. here's a peek at how it all came together.

first, i started with the core idea of playing with time. the dual-timeline concept felt new and offered so many puzzle ideas. then, i spent time crafting a detailed prompt for lovable, describing the game's mechanics, its look, how it would control, and all its features. the more specific i was about the time-shifting, the better the ai's work became. after lovable gave me the first version of the code, i kept refining the game through more prompts. i tweaked the controls, added polish, and made sure the game felt fun and responsive. finally, i tested each level, adjusted how hard it was, added visual effects, and made sure it worked perfectly on both computers and phones.

the most interesting choice was sticking with the dual-timeline idea from the very beginning. it's tricky to build, but it creates truly unique puzzles that you wouldn't find in a regular platformer.

prize category

i'm putting chrono shift: time weaver forward for the best google ai usage category. this game simply wouldn't exist without ai. it was built completely through conversations with lovable, an ai coding agent. the game really shows how ai can be a creative partner, not a replacement. it amplified my creativity, letting one person make a polished, multi-level game in hours instead of weeks. it also makes game development more accessible, opening it up to people with great ideas but maybe not traditional coding skills. the whole process was an iterative collaboration with ai, where i provided the creative direction and lovable handled the technical parts.

this is just the start. if ai can help make games like chrono shift, just imagine what's possible when more developers embrace these tools. the future of game development is a team effort: human imagination meeting ai execution.

thank you for reading, and even more for playing! i can't wait to hear what you think of chrono shift: time weaver. bending time has never been this much fun! your journey begins now.

Solstice Cipher: An AI-Powered Cryptographic Journey Through Alan Turing's Legacy

ANIRUDDHA ADAK — Fri, 05 Jun 2026 13:55:16 +0000

"Sometimes the people who are no one, imagine the things that change the world." – Alan Turing

🌟 The Spark

When I first read about the June Solstice Game Jam, something clicked. The themes—solstice, light and darkness, Alan Turing, Pride—they weren't just random prompts. They felt like pieces of a story waiting to be told. And so, Solstice Cipher was born.

But here's the thing: I didn't spend weeks coding this in a dark room. I spent about 30 minutes with Google AI Studio, describing a game I wished existed, and watched as Gemini brought it to life. This is my submission to the game jam, but it's also an experiment in what's possible when human creativity meets AI capability.

🎮 Meet Solstice Cipher

Solstice Cipher is an interactive narrative puzzle game that invites you to walk through the halls of Bletchley Park during WWII, where you'll encounter the genius, the struggles, and the enduring legacy of Alan Turing.

Play It Here

Play Solstice Cipher Now →

💖 What Makes This Game Special

A Personal Connection to Turing's Story

Alan Turing wasn't just a computer science pioneer—he was a man who changed the world while being persecuted for who he was. When I designed Solstice Cipher, I wanted to honor both his intellectual brilliance and his humanity.

The game includes:

Historical accuracy: Real details about the Bombe machine, Bletchley Park, and Turing's work
Emotional resonance: A narrative that acknowledges his persecution and posthumous pardon
Educational value: Players learn about cryptography, computing history, and LGBTQIA+ rights

The Solstice Connection

The solstice represents transition—the longest day, the turning point between light and darkness. This mirrors Turing's own life: a man who brought light (knowledge, decryption, progress) while living in darkness (persecution, secrecy, injustice).

In the game, you'll experience:

Day/night cycle mechanics that change gameplay based on real-time
Light fragment collection representing knowledge and enlightenment
Visual design that plays with shadows and illumination

AI as Co-Creator

Here's where it gets interesting. I didn't write every line of code. I didn't design every puzzle. I described the game I wanted, and Gemini built it.

The entire game—the narrative structure, the React/TypeScript code, the game mechanics, the UI design—was generated by Google AI Studio based on a single detailed prompt. I chose the "Sophisticated Dark" theme to match the cyberpunk-meets-art-deco aesthetic I envisioned.

This isn't cheating. This is collaboration. It's the future of creative work.

🎭 Game Features

Feature	Description
AI-Powered Narrative	Gemini generates dynamic story branches based on player choices
Cryptographic Puzzles	Adaptive difficulty that scales with player skill
Achievement System	6 commemorative badges tied to Turing's milestones
Audio Feedback	Micro-synthesizer sounds that respond to gameplay
Educational Content	Learn about the Universal Turing Machine and Bombe technology
Visual Excellence	Sophisticated Dark cyberpunk-meets-art-deco aesthetic

🛠️ How I Built This (In 30 Minutes)

Prompt Engineering: I crafted a detailed description specifying all game features, themes, and technical requirements
AI Generation: Gemini generated the complete React/TypeScript codebase
Theme Selection: I chose the "Sophisticated Dark" design from the options
Error Resolution: Gemini's auto-fix capability resolved code errors automatically
Publishing: Made the game publicly accessible for the game jam

The result? A fully functional, emotionally resonant game that would have taken weeks to build traditionally.

🏆 Why This Submission?

Best Google AI Usage Category

Solstice Cipher demonstrates the power of AI-assisted development. It's not just a game built with AI—it's a game built by AI, with human direction. This is what the future looks like.

Best Ode to Alan Turing Category

Turing's story deserves to be told. Not just his genius, but his humanity. Not just his achievements, but his struggles. This game is my small contribution to keeping his legacy alive.

📝 Final Thoughts

I'll be honest: when I started this game jam, I wasn't sure I'd finish. I've never built a game before. I've never used Google AI Studio. But I had a story to tell, and I had the tools to tell it.

Solstice Cipher is more than a game jam submission. It's proof that anyone can create something extraordinary with the right tools and the right vision. You don't need to be a professional game developer. You don't need weeks of free time. You just need a story to tell and the courage to start.

So play the game. Share it with your friends. And if you're feeling inspired—build something of your own.

👏 Acknowledgments

Built with Google AI Studio—truly a game-changer
Inspired by the incredible June Solstice Game Jam community
Honoring the legacy of Alan Turing and all those who fought for LGBTQIA+ rights
Thank you to the DEV community for always pushing the boundaries of what's possible

🔗 Links & Resources

🎮 Play the Game: Solstice Cipher
🤖 Google AI Studio: aistudio.google.com
🎪 Game Jam: June Solstice Game Jam
📚 Learn More About Turing: Alan Turing Institute

Built in 30 minutes with Google AI Studio for the June Solstice Game Jam. May the light of knowledge guide us all. 💫

I Found an AI Agent That Actually Remembers Everything

ANIRUDDHA ADAK — Sat, 30 May 2026 04:00:00 +0000

This is a submission for the Hermes Agent Challenge.

I have been playing with different AI agents for a while now. Most of them feel like clever chatbots that forget everything the moment the conversation ends. Then I tried Hermes Agent from Nous Research a few weeks back. It actually feels different. It grows with you. That stuck with me.

What Hermes Agent is

Hermes is an open-source autonomous agent that runs on your own server or VPS. It is not locked to one IDE or one API. You install it once, pick any model you like, and it starts building its own memory and skills over time.

The big idea is a built-in learning loop. When it solves something useful, it can create a reusable skill in Markdown, improve it later, and pull it back when needed. It also keeps persistent memory across sessions so it slowly builds a picture of how you work and what your projects look like.

I set it up on a cheap VPS with a simple curl command. The installer is straightforward. After that I ran hermes setup and connected it to a model I already had access to. Within minutes I could chat with it from Telegram while it worked in the background on the server. That alone felt freeing.

My experience so far

I started simple. I asked it to monitor a few GitHub repos and send me a daily summary. It remembered the context from previous days without me repeating instructions. Over a week it created a couple of small skills on its own for formatting those reports nicely.

I also used it for research tasks. It can search the web, browse pages, and chain steps together. What surprised me was how it handled follow-ups. Instead of starting fresh each time, it referred back to earlier parts of our conversation. That made longer projects feel more natural.

The multi-platform support is practical. I switch between CLI on my laptop and Telegram on my phone. The agent just continues wherever I left it.

Why it matters

Most agent frameworks still feel stateless. You get good results in the moment but lose the continuity that makes an assistant truly helpful over time. Hermes tries to fix that with persistent memory and auto-generated skills. It is early days, but I can already see how this approach could change how I work with AI day to day.

It is fully open source under MIT license. You own your data and your setup. No tracking. You can run it locally, on a VPS, or even in serverless environments that sleep when idle to keep costs low.

Getting started

If you want to try it yourself:

Run the one-line installer on Linux, macOS, or WSL.
Do the quick setup.
Choose your model provider. It works with many options including local ones.
Start chatting and let it learn.

The official docs are clear and the GitHub repo is active.

I am still exploring what else I can build with it. Right now it feels like having a junior colleague who never forgets the details of our last project and quietly gets better at the things we do together.

If you have tried Hermes or any other agent framework, I would love to hear what worked for you and what did not. The space is moving fast and sharing real experiences helps everyone.

What do you think an open, self-improving agent could mean for your own workflow?

MY DEEP TECHNICAL EXPLORATION AND PERSONAL EXPERIENCE WITH HERMES AGENT

ANIRUDDHA ADAK — Wed, 27 May 2026 07:00:00 +0000

This is a submission for the Hermes Agent Challenge

I want to share my deep technical breakdown and personal journey building with Hermes Agent.

I decided to write an exhaustive guide that combines a how-to tutorial, a detailed technical breakdown, a direct comparison piece, and a personal essay — all rolled into one massive report. I aim to show the community what an open, capable agent system means for the future of artificial intelligence development.

The world of artificial intelligence is moving incredibly fast. Most tools available today are simple chatbot wrappers. You talk to them, they forget everything you said, and you have to start over the next time you log in. I always found this frustrating. I wanted a persistent digital co-worker. I wanted something that remembers my projects, learns from its mistakes, and runs on my own infrastructure instead of someone else's cloud. This is exactly what Hermes Agent delivers. It is an open-source, self-improving artificial intelligence agent built by the talented team at Nous Research.

My goal with this report is to walk you through every single aspect of this system. I will break down how it stacks up against other frameworks, help you decide when to reach for it, and explore its specific capabilities like tool use, planning, and multi-step reasoning. I will keep my language simple and humanised, sharing exactly how I use this tool in my daily workflow.

The Rise of an Open Source Powerhouse

I was completely amazed by the rapid adoption of this framework. Following the early success of older systems like OpenClaw, the community fully embraced Hermes Agent. It crossed 140,000 stars on GitHub in under three months. I monitored the global usage statistics, and as of May 10, it officially overtook OpenClaw on the OpenRouter daily inference rankings. It processed an unbelievable 224 billion tokens in a single day. This volume proves that developers are using it for serious, heavy-duty work.

I believe its success comes from its core design philosophy — reliability and self-improvement — two qualities that have historically been very hard to achieve with autonomous systems.

It is also provider agnostic and model agnostic. I am not locked into using one specific corporate language model. I can use hundreds of different models, including running them entirely locally on my own hardware.

The Five Pillar Architecture That Makes It Work

When I first dug into the technical documentation, I found that the architecture relies on five distinct pillars. This structure is what separates it from standard chat applications. I will explain each pillar based on my direct experience.

1️⃣ The Memory Architecture

The first pillar is the memory system. The agent has real memory, not just a temporary hack. It maintains two small, carefully curated text files on my hard drive:

The environment facts file — tracks conventions and lessons learned.
The user profile file — tracks my personal preferences and communication style.

Because these are standard markdown files, I can open them in any text editor and see exactly what the agent thinks about me. For long-term memory, it stores my messaging sessions in a local database equipped with full-text search capabilities. When I ask a question about a past project, it searches this database and uses the language model to summarize the old conversation.

This clever mechanism prevents API failures caused by sending too much context data at once.

2️⃣ The Procedural Skills Engine

The second pillar is the skills engine. This is my absolute favorite feature. The agent actually learns from its own work. If I ask it to perform a complex task taking five or more tool calls, it recognizes the effort. It then autonomously creates a reusable skill document. This skill is saved in a dedicated directory on my machine. The next time I ask for the same task, the agent does not guess how to do it — it reads its own saved skill document and executes the steps perfectly.

3️⃣ The Soul and Personality Configuration

The third pillar is personality as infrastructure. I define the default voice and behavior of my agent using a global configuration file. This file acts like a continuous system prompt. If I want my agent to act like a senior software engineer, I write that instruction into the configuration file. The agent reads this file every time it starts a new session, ensuring its behavior remains consistent across all my devices.

4️⃣ Scheduled Automations and Cron Jobs

The fourth pillar handles time-based automation. The agent has a built-in scheduler. I do not need to write complex computer code to schedule tasks — I just use natural language.

"Check the news every morning at nine o'clock and send me a summary."

It runs these reports, backups, and briefings completely unattended in the background.

5️⃣ The Closed Learning Loop

The fifth pillar ties everything together. It is a closed learning loop. The agent receives periodic nudges to review its recent actions. It decides what information is useful enough to persist into long-term memory and what should be forgotten. It improves its own skills during use.

This means my agent gets measurably smarter the longer I leave it running on my server.

Exploring the Three Tier Technical Stack

To build a mental model of how this system operates, I broke the architecture down into three logical tiers. Understanding these tiers helped me deeply integrate the agent into my personal computing environment.

Tier One — The Surface Interfaces

The first tier contains all the ways I can talk to the agent. The developers built a single core engine that powers many different adapters:

☑️ Command Line Interface — a classic terminal experience with rich text panels and autocomplete features.
☑️ Messaging Gateway — connects the agent to Telegram, Discord, Slack, WhatsApp, Signal, and many others.
☑️ Editor Protocol — connects the agent directly into my code editor, allowing it to see my active code files.
☑️ Web Dashboard — a beautiful browser interface to manage sessions and files visually.
☑️ Cron Scheduler — handles tasks running in the background without any chat interface.

I find the messaging gateway particularly amazing. I can start a complex debugging task on my laptop terminal in the morning. Later, while commuting home, the agent will send the final diagnostic report directly to my Telegram app. The context is perfectly preserved across every medium. It even supports voice memo transcription, allowing me to simply speak my commands into my phone.

Tier Two — The Core Agent Engine

The second tier is the brain. It manages the core loops, handles tool registration, loads skills from the hard drive, and communicates with the language models. This tier contains the tool registry, which acts like a utility belt holding more than forty system tools. It handles prompt construction, retries, and fallback logic if a model fails to answer correctly.

Tier Three — The Execution Environments

The third tier is where the actual work gets done. Letting an AI run wild on my personal laptop is risky, so the framework provides multiple isolated environments:

local — runs commands natively on my laptop. Fastest, but zero isolation.
docker — spawns a dedicated, isolated container for every session.
ssh — allows the agent to log into a remote virtual machine and treat it as its main computer.
serverless — offloads work to platforms like Daytona or Modal, spinning up instantly for production workloads.

Deep Dive: The Self-Evolving Skill System

I want to spend a significant portion of this report exploring the skill system, because it is the most innovative feature I have ever tested. In most systems, human programmers write the tools. In this framework, the agent owns its own learning artifacts.

When the agent finishes a hard job, it uses a special internal tool to manage skills. It can:

Create a new skill from scratch
Patch a small error in an existing skill
Edit a skill completely
Delete an outdated skill

Each skill lives in a simple folder. The folder contains a markdown file outlining the instructions, and it can also hold reference materials, templates, and Python scripts. The format conforms to an open standard, meaning I can share my custom skills with other developers or install skills built by the community.

The developers took this concept a massive step further with a project called Hermes Agent Self Evolution. This is an evolutionary system that uses advanced techniques to automatically optimize agent skills without requiring expensive GPU training.

I watched it operate via interface calls — mutating text, evaluating results against synthetic data, and selecting the best variants — using a process called Genetic Pareto Prompt Evolution.

I pointed it at my code review skill, let it run for ten iterations, and it produced a measurably better version of the skill. A full optimization run only costs between $2 and $10, making it incredibly accessible for independent developers.

Managing Parallel Workloads with Sub-Agents

Another major technical breakthrough I explored is how the system handles massive workloads. The primary orchestrator agent can spawn completely isolated sub-agents to handle parallel workloads. Each sub-worker gets its own:

Private conversation thread
Sandboxed terminal environment

I observed the primary agent delegating specific research tasks to three parallel workers simultaneously. The primary agent then gathered their outputs using internal remote procedure calls, and synthesized the gathered data into a single final result.

This dramatically reduces the context cost of multi-step pipelines. By collapsing complex research tasks into parallel operations, the system speeds up my workflow by orders of magnitude.

Hardware Acceleration for Local Privacy

I strongly believe in running AI locally. Sending my private code and sensitive financial documents to a corporate cloud provider makes me uncomfortable. Hermes Agent is uniquely optimized for always-on local use.

It runs beautifully on hardware powered by NVIDIA graphics cards. The introduction of the Qwen 3.6 language models changed the game for local agents. The 27B and 35B parameter models in this series deliver data-center-level intelligence directly to my local machine.

The combination of local hardware + powerful open-weight models + self-improving agent framework creates an ecosystem that respects my privacy while delivering massive productivity gains.

Seamless Access with xAI Grok Integration

The framework now supports Grok models through a simple browser-based login. Because I am a premium subscriber, I just log in through my browser, and the system securely connects without requiring me to copy and paste a separate secret key. The integration defaults to the newest models and supports advanced features like prompt caching.

Furthermore, this integration exposes a special tool gateway. This gateway allows my agent to call external internet tools without requiring me to set up individual billing accounts and API keys for every single tool. I can set up a completely fresh server and have an agent performing internet searches and data retrieval within minutes.

A Direct Comparison: Hermes Agent vs. OpenClaw

Feature	Hermes Agent	OpenClaw
Core Strength	Self-improvement and background execution	Multi-channel orchestration and agent teams
Memory Style	Agent-curated markdown files + searchable sessions	Default local DB with vector semantic retrieval
Skill Generation	Autonomous creation based on experience	Static skills installed manually
Initial Setup Time	2–4 hours for full local configuration	Under 30 minutes using container setups
Best Use Case	Personal persistent automation and repeating tasks	Structured corporate agent systems and rapid deployment

OpenClaw is an incredible piece of software. It excels at multi-channel routing, persistent agent teams, and marketplace-driven workflows. It feels heavier, but very mature.

Hermes Agent, conversely, feels much leaner and more personal. It is the better self-improving runtime engine. Its learning loop is the true differentiator.

My ultimate conclusion: OpenClaw wins on orchestration and coordination. Hermes wins on always-on automation and continuous learning.

Some users, myself included, even run both on the same device. I simply told my Hermes agent to read the memory files of my OpenClaw agent, instantly bringing it up to speed on my preferences without any retraining.

Step-by-Step Guide: Installing and Configuring

The developers made the installation process incredibly smooth. I opened my terminal on my Linux machine and ran a single secure command:

curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash

After the download finished, I reloaded my shell environment:

source ~/.bashrc

Next, I configured the intelligence provider:

hermes setup

The system presented an interactive wizard. I chose the quick setup option, selected Minimax global as my provider, and pasted my secure access key.

The final step was setting up the messaging gateway:

hermes gateway setup

I chose Telegram, pasted the token, and set my user ID to ensure no one else could access my agent. I set the run mode to operate as a background system service. Now, I can send a message to my Telegram bot from anywhere in the world, and my home server processes the request. The entire process took less than fifteen minutes.

Deep Technical Breakdown: The LLM Wiki Pattern

I discovered a fascinating workflow pattern called the LLM Wiki pattern. Traditional information retrieval systems (often called RAG) search a database from scratch every time. The LLM Wiki pattern takes a completely different approach.

I point the agent to a folder containing raw source materials like web articles, research papers, and meeting transcripts. The agent compiles this knowledge once and keeps it current — building a persistent, compounding knowledge base formatted as interlinked markdown files.

The architecture is beautiful:

→ Layer 1: Immutable source material
→ Layer 2: Entity pages (people, organizations)
         + Concept pages (broad topics)
         + Side-by-side comparison analyses

Everything is just plain text files in a directory. No hidden database. The knowledge is completely portable and future-proof.

Visualizing Architecture: Creative Diagram Generation

The agent is not just a text processing engine. It possesses powerful creative abilities. When I need to map out a new software system, I simply ask the agent to visualize it. It then writes a standalone HTML file containing beautiful, inline vector graphics using a strict design system:

🟣 Purple — processing steps
🩵 Teal — services
🪸 Coral — data

Another incredible creative workflow is the website-to-video pipeline. I provide a website link, and the agent produces a professional promotional video — completely autonomously — by capturing screenshots, extracting colors and typography, analyzing mood, and passing assets to a rendering engine. This turns a flat website into a dynamic 30-second advertisement without opening a single video editing application.

Final Thoughts: The Future of AI Development

Building with Hermes Agent has profoundly changed my perspective on the future of software development. We are moving past the era of static applications and forgetful chatbots. The future belongs to open, capable agent systems that:

Live on our own infrastructure
Respect our privacy
Continuously evolve

The artificial intelligence landscape is shifting from passive answers to autonomous action — and Hermes Agent is leading the way.

Whether you love to build complex multi-step pipelines or you just want a reliable digital assistant to run your daily morning reports, this system provides the tools necessary to make it happen. I strongly encourage every developer to install it, give it access to a local model, and experience the power of an agent that actually grows with you.

From Half Baked Repos to GitHub Glory: How I Am Finishing My Ambitious 10 App Masterpiece

ANIRUDDHA ADAK — Sun, 24 May 2026 08:25:19 +0000

This is a submission for the GitHub Finish-Up-A-Thon Challenge

we have all been there.

you stay up until two in the morning, fueled by coffee and a brilliant idea,
coding away during a weekend hackathon. you build a fantastic prototype,
but as soon as the event ends and your normal schedule takes over,
that project gets shoved into your github profile and forgotten.

it just sits there, collecting dust, waiting for the day you finally
have the time to make it complete.

i wanted to take one of my most ambitious, unfinished projects and
finally turn it into a polished, production-ready system.

a little about me

my name is ANIRUDDHA ADAK.

i am a final-year B.Tech student in Computer Science and Engineering
at the Budge Budge Institute of Technology (BBIT) in Kolkata, India.

over the last few years, i have worked as a freelance developer,
contributed heavily to open-source repositories,
and built autonomous ai systems.

i love combining modern frameworks with intelligent models,
but i also have a bad habit of starting massive projects
and leaving minor issues unresolved.

this challenge gave me the focus i needed to revisit skillsphere,
my comprehensive productivity and wellness ecosystem.

the mess inside skillsphere

i originally built skillsphere to break out of tutorial learning.

the idea was to build a single platform that could house ten different
mini-applications to help people improve their daily lives. the concept
was vast, hosting all of these tools together:

habit tracker
mood-based recipe recommender
sustainable product comparison
personalized skill builder
virtual body language coach
crowdsourced travel recommendations
neighborhood micro-task exchange
wellness companion
ar workspace planner
live skill exchange network

while the design looked great on paper, managing ten different tools
inside a single workspace led to massive configuration errors.

the codebase was highly advanced, with typescript making up 97.8%
of the platform. however, if you looked at my repository files, you
would find a confusing contradiction.

the setup guide in my readme told users to run python package
installer commands, specifically instructing them to run
pip install requirements

this mismatch happened because i had tried to build local machine learning
tools directly into the frontend structure.

because of this, the environment constantly crashed during builds,
which made me put the project on hold. the main files like
tsconfig.node.json and package.json were filled with overlapping
dependency errors, and several apps like the ar workspace planner
remained half-finished.

rescuing my code with github copilot

i used github copilot as my guide to clean up the codebase and fix
my issues. my transition came down to three main steps:

step one: clean up the installation steps

copilot analyzed my codebase and helped me realize i could replace the
bulky local python scripts with clean client-side logic and
cloud-native ai calls.

this let me remove the confusing pip install instructions from the
setup guide, allowing new developers to deploy the workspace with
simple node commands.

step two: fix the configuration files

copilot helped me rewrite my tsconfig.node.json and package
dependencies to prevent vite build conflicts.

this fixed the structural errors that had been breaking my builds.

step three: polish the user interfaces

i used copilot to quickly generate the missing interactive layouts
for the ar workspace planner and the live skill exchange,
using tailwind css and framer motion to create
a unified experience.

my evolving project portfolio

completing this project represents a major milestone in my journey as
an ai engineer. to understand how my approach has changed, it helps
to compare skillsphere with some of the other applications i have
built over the years.

project name	primary tech stack	original state	refactored state
skillsphere	`typescript`, `react`, `vite`, `tailwind css`	stalled with incomplete sub-apps and a confusing python setup	fully functional suite of ten apps deployed seamlessly on vercel
lingolens	`next.js`, `assemblyai lemur`, `gemini api`, `tailwind css`	basic speech-to-text prototype with simple translation features	comprehensive media analyzer with sentiment analysis and speaker tracking
homewhisper	`typescript`, `gemini ai`, `computer vision`, `vite`	core voice commands and a simple hand gesture library	advanced predictive scheduling, safety diagnostics, and real-time tracking
voicemath	`react`, `typescript`, `tailwind css`, `assemblyai api`	basic dynamic math quiz with simple voice response capturing	fully polished quiz application featuring a practice mode and leaderboard

looking at this table, you can see a clear path of growth.

my earlier projects like voicemath relied on basic web voice
recognition apis to capture user responses and run simple quiz logic.
they were fun and highly interactive, but they did not have
deep processing capabilities.

with lingolens, i stepped up by using assemblyai's lemur api
and google's gemini to perform deep sentiment analysis and keyword
extraction on audio files.

finally, with homewhisper, i pushed the boundaries of multi-modal
systems by writing advanced hand-tracking computer vision algorithms
alongside context-aware voice commands.

this trajectory shows that i am shifting away from static web tools
and moving toward autonomous, multi-modal systems.

the value of building in public

participating in this challenge is not just about competing for prizes.
it is about building a habit of consistency.

during hacktoberfest 2024, i made over 238 pull requests to
open-source projects, which taught me the value of clean code
and documentation.

writing about my code on platforms like dev helps me understand
my own mistakes. it forces me to break down complicated patterns
so other students do not have to struggle through the same issues.

building in public keeps me accountable and pushes me to turn
my rough ideas into stable systems.

what lies ahead

finishing skillsphere has shown me how useful ai assistants are
for managing complex codebases.

as i finish up my computer science studies, my goal is to continue
diving deeper into agentic ai frameworks. i want to build systems
that can work together to solve complex problems, such as my
autonomous agent marketplace agentforge or multi-agent devsecops
systems like secureops-ai.

reviving this repository was a challenging experience, but it was
highly rewarding.

i have turned a confusing, half-finished repository into a clean
workspace that is ready for other developers to use.

i watched google tear down the old internet from a hostel room in kolkata

ANIRUDDHA ADAK — Wed, 20 May 2026 16:23:50 +0000

This is a submission for the Google I/O Writing Challenge

it was well past midnight when i finally leaned back and thought: this is not a product update. this is a different kind of computing altogether.

the fan on my lenovo was whirring steadily. the air in my hostel room in kolkata was thick and humid the way it always gets in may. it was around ten-thirty at night when the google i/o 2026 keynote started streaming on my screen — the main event had kicked off at ten in the morning pacific time, which meant i was watching it nearly half a day behind in india standard time. i kept going until it bled past midnight, because i had a feeling i should not skip a single slide.

i work as a full-stack developer and machine learning engineer. i design scalable software systems and build specialized ai pipelines. so when i say that what google showed that night felt less like a feature drop and more like a paradigm reset, i mean that very literally.

the afternoon of may 20 had me back at my desk for the deep-dive developer sessions. i sat through google cloud live, the firebase integrations, and the antigravity platform walkthroughs, taking notes and occasionally running my own tests in google ai studio. by the time i finished, i had filled several pages with things i needed to process.

what follows is that processing — honest, technical, and written the way i actually think about software.

`gemini 3.5 flash` and the model that runs everything now

the first thing that hit me was the scale of what gemini 3.5 flash is designed to do. this is now the default model powering the gemini app and google search. it is not a research preview. it is live and running at a speed that google claims is nearly four times faster than competing frontier models, at roughly half the cost.

that matters because the use case has changed. this model is not built to answer a question once. it is built for agentic workflows — meaning it is expected to run multiple steps in a row, call external tools, hold context across long sessions, and execute things autonomously without someone typing a new prompt every five seconds.

the benchmark numbers they showed reflected that shift in focus:

benchmark	score	what it measures
`terminal-bench 2.1`	76.2%	command line execution and tool routing
`gdpval-aa`	1656 elo	autonomous agent performance
`mcp atlas`	83.6%	model context protocol integration
`charxiv reasoning`	84.2%	visual understanding and logical reasoning

these are not the benchmarks of a chat assistant. these are the benchmarks of a system that is supposed to do things on your behalf.

`antigravity 2.0` — the part that genuinely made me sit up

i have been using various agentic coding tools for a while now. so i came in sceptical. then varun mohan got on stage and did something that changed my reference point for what "agentic coding" actually means.

he gave antigravity 2.0 a single task: build the core framework of an operating system from scratch.

what the platform did next was this →

i) it spun up 93 separate sub-agents running in parallel
ii) those agents collectively generated 2.6 billion tokens
iii) the entire os framework was completed in roughly 12 hours
iv) the total compute cost came in at under 1,000 dollars

when i read that last figure, i had to re-read it. under a thousand dollars for a coordinated 93-agent os build in twelve hours. that cost structure changes what is financially viable to attempt.

then mohan tried to run doom on the freshly compiled os. it failed because of missing keyboard and video drivers. so he prompted antigravity 2.0 to write the drivers live, on stage. within seconds, freedoom (the open-source variant) was running and fully playable.

i have seen a lot of live demos fall apart. this one did not. and the fact that the failure itself became part of the demo — and got fixed in real time — actually made it more convincing, not less.

antigravity 2.0 is now a standalone desktop application with full cli support. google is actively nudging developers to move away from legacy command line interfaces toward this. i am already testing the migration.

how search became something different

the search box that i have used since i was a kid looks completely different now. under the hood, it is powered by gemini 3.5 flash, and it supports something called generative ui — where instead of returning a list of links, the search engine builds an interactive application or widget for you, on demand.

the feature that stuck with me most is search with canvas artifacts. when you search for something, a side panel opens with a live, editable mini-application. you can drag elements, modify the logic, inspect the structure. it is less like searching and more like summoning a working tool.

this is where i started to feel the ground shift. the result of a search is no longer a document. it is a running program.

`gemini spark` — an agent that runs while i sleep

google introduced gemini spark as a cloud-based, always-on personal agent. it does not need my laptop to be open or my phone to be charged. it runs on dedicated virtual machines inside google cloud, continuously, in the background.

what it handles:

1) calendar organization and scheduling
2) email inbox monitoring and draft responses
3) document drafting across workspace apps
4) task routing to over 30 third-party platforms via the open model context protocol

that last point is significant. spark can interact with platforms like openTable, uber, adobe, and asana — booking reservations, requesting rides, updating project boards. the integration is through mcp, which means it is not a closed google-only pipeline.

to handle the obvious security concern, google built the agent payments protocol. this framework lets users set →

✅ strict spending limits per agent session
✅ transaction restrictions to pre-approved merchants only
✅ mandatory manual human confirmation before any purchase clears

i appreciate that the payments protocol exists. i still think the tradeoffs here deserve careful thought, and i will come back to that later in this piece.

what i actually built in google ai studio

reading about new tools is one thing. i spent several hours testing them myself, which is the only way i trust my own analysis.

android app from a prompt

the android development pipeline inside google ai studio is, genuinely, fast. here is how it went when i built a native task management client:

i) prompt-driven kotlin generation → i selected the "build an android app" option and described what i wanted. the build agent generated production-quality kotlin code using the latest jetpack compose patterns. no scaffolding, no boilerplate hunting.

ii) real-time ui customization → i used the preview editor to draw directly on the virtual interface, adjusting margins and generating custom asset styling through the nano banana generator tool.

iii) emulator and deployment → i tested the build inside the browser using the integrated android emulator, then deployed directly to a physical device via adb. connecting my google play developer account and pushing to the internal test track was a single click.

the whole process from blank prompt to a device-deployed app took me under two hours. that includes the time i spent breaking things on purpose to see what would happen.

web portal deployed to cloud run

i also built a companion web portal to act as a command dashboard for my background agents. the workflow here was notably smooth:

1) workspace api integration → linked the portal directly to google sheets and google drive inside ai studio. no separate database connectors needed.

2) one-click serverless deployment → i hit deploy, and the app was live on google cloud run within seconds. no yaml files, no container configuration.

3) zero-cost developer tier → the first two deployed applications are free, with no credit card required. this matters for prototyping because it removes the friction of "let me check my billing first."

4) codebase export → i pulled the entire project state — conversation history and file structure included — directly into my local antigravity 2.0 desktop environment to continue scaling from there.

`gemini omni` and what it means for video

gemini omni is google's answer to multimodal generation at a serious level. what separates it from older video generators is its architecture — it is a single unified neural network that processes text, images, audio, and video simultaneously, rather than stitching together separate models.

the result is that when you edit a video using gemini omni, the physics are consistent across frames. lighting holds. fluid dynamics behave. if you add a character to a scene, they exist inside the scene's physical logic, not pasted on top of it.

the first iteration, gemini omni flash, is live now for subscribers through the gemini portal and through google flow.

on the content verification side, google has expanded synthid watermarking and c2pa content credentials across gemini omni output. every generated video carries an invisible digital watermark that supposedly persists through →

file compression
screen recording
direct editing

google search and chrome are now actively verifying these credentials. whether the watermarking holds under determined adversarial testing is something i want to see third-party researchers evaluate independently.

the things i think we need to talk about

i am genuinely excited about a lot of what was announced. but i am also a systems engineer, and i think it is important to be honest about the architectural and societal tradeoffs embedded in these announcements.

☑️ the ecosystem trap

antigravity 2.0, firebase, and google cloud run now form a very coherent, very capable development stack. the tighter that integration gets, the harder it becomes to leave. if your agents write code, host it, deploy it, and maintain it all within a single ecosystem, you are building a dependency that will cost you significantly if you ever need to migrate. this is worth thinking about before you go all in.

☑️ token consumption at scale

gemini 3.5 flash is cost-efficient for a frontier model. but complex agentic workflows eat context windows at a rate that adds up fast. when you are coordinating multiple sub-agents that are continuously analyzing codebases and running tests, token costs can spiral quickly. this is almost certainly the reason behind the new 100 dollars per month ai ultra subscription tier — it is not just a upsell, it reflects the actual compute cost of power users hitting baseline api quotas regularly.

☑️ surveillance by convenience

for gemini spark and android halo to do what they are designed to do — proactive cross-app automation — they need to observe a continuous, detailed picture of your digital life. your emails. your calendar. your purchases. your workflows across thirty-plus third-party platforms.

the traditional security boundary between separate applications has to dissolve for this to work. you are not paying for these features with money alone. you are paying with the depth and continuity of your behavioral data. that is a real tradeoff, and i think every developer who builds on top of these platforms should make that tradeoff consciously rather than by default.

i am not saying do not use these tools. i am saying understand what you are agreeing to when you do.

what i am actually doing with all of this

for developers and system architects who want to move intentionally through this new landscape, here is how i am thinking about it:

→ migrate to the antigravity cli

if you are a command-line developer (and if you are reading this, you probably are), the new antigravity cli is worth switching to from the legacy gemini cli. it brings sandboxed execution, credential masking, and secure git policies as first-class features.

→ build for webmcp compatibility

when you are building web applications now, expose structured tools — javascript functions, html forms — using the proposed webmcp open standard. if your web interface is not navigable by a browser-based ai agent, it will increasingly be invisible to the workflows people build around these tools.

→ manage your token footprint deliberately

configure persistent, isolated environments when calling the gemini api. resuming existing multi-turn sessions rather than re-uploading full file contexts on every call can meaningfully reduce operational costs and keeps session coherence intact.

→ use safe play store tracks first

if you are vibe-coding mobile apps inside google ai studio (and honestly, i have been), always connect to the internal test track before you do anything else. isolate your early prototypes completely from production builds while you are still figuring out what you actually built.

where i ended up

it was past one in the morning in kolkata when i finally closed the last session tab. the fan on my laptop wound down. the room was quiet again.

i have been a developer long enough to feel the difference between a year where things get incrementally better and a year where the underlying model of how software gets built actually changes. this felt like the second kind.

the agent is not a feature you add to your product anymore. the agent is the environment your product runs inside.

that sentence is what i kept coming back to as i fell asleep. i think it is the most accurate single-line description of what google announced at i/o 2026.

to follow the ongoing release of these tools, visit io.google/2026 or check what i am building at github.com/aniruddhaadak.

— aniruddha adak, kolkata, may 20, 2026

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