DEV Community: Nimesh Kulkarni

AI Is Not Just Replacing Work, It Is Challenging How We Think

Nimesh Kulkarni — Sat, 27 Jun 2026 13:25:58 +0000

For a long time, we treated AI like a tool.

Something that could autocomplete code, summarize long documents, generate images, or answer simple questions. It felt useful, but still limited. The human was always the thinker, and AI was just the assistant.

That line is slowly becoming blurry.

Today, AI can reason through problems, debug code, write product strategies, explain complex systems, generate architecture diagrams, and even challenge our assumptions. It does not just give answers anymore. It can break down problems in a way that looks very close to thinking.

For developers, this is both exciting and uncomfortable.

We used to believe that writing syntax was the main skill. Then AI made syntax cheap. Now we say problem solving matters more. But AI is also getting better at problem solving. It can compare approaches, suggest tradeoffs, identify bugs, and explain why one solution is better than another.

So the real question is not whether AI will overtake human thinking completely.

The better question is: what kind of thinking will still matter?

Blind execution will lose value. Copying tutorials will lose value. Writing basic CRUD apps will lose value. But taste, judgment, creativity, clarity, and understanding real human problems will become more important.

AI can generate ten solutions in seconds, but someone still has to decide which one is actually useful. AI can write code fast, but someone still has to understand the product, the users, the constraints, and the long term impact.

The future developer will not be the person who simply writes code.

The future developer will be the person who knows what to build, why it matters, how to guide AI properly, and how to judge the output with real understanding.

AI may overtake many thinking tasks, but it will also expose who was truly thinking in the first place.

The people who only memorized steps will struggle.

The people who understand systems, ask better questions, and keep learning will become more powerful than ever.

How AI Is Quietly Automating 70% of Customer Support

Nimesh Kulkarni — Wed, 10 Jun 2026 13:57:05 +0000

Customer support automation used to mean rigid chatbots, scripted menus, and irrelevant help centre links.

That is changing fast.

Platforms like Zendesk, Intercom, and Freshworks now use large language models to understand customer questions, search company knowledge, complete basic actions, and escalate complex issues to human agents.

In some deployments, these platforms report automation or resolution rates near 70%.

That does not mean 70% of support jobs have disappeared. It means a large share of repetitive customer conversations can now be resolved without an agent manually handling every step.

What AI Support Agents Can Do

Modern AI agents can:

Understand natural language
Identify customer intent
Search approved documentation
Generate contextual answers
Check order or account information
Process simple requests
Summarize conversations
Transfer difficult cases to humans

Unlike traditional chatbots, they do not depend entirely on fixed decision trees.

A customer can write:

«I paid for the upgrade, but my account is still on the free plan.»

The AI can understand that this is not just a general billing question. It may need to verify the payment, check the subscription status, and escalate the case if the account was not updated correctly.

Why Support Is Easy to Automate

A large percentage of support volume comes from repetitive questions:

How do I reset my password?
Where is my order?
How do I download an invoice?
Can I cancel my plan?
How do I add a team member?

These questions are important, but they usually follow predictable patterns.

AI is good at understanding the request, retrieving the correct information, and presenting it clearly. When connected to business systems, it can also complete the task instead of simply explaining it.

The Human Role Is Changing

AI will not remove the need for human support.

It will change what human agents handle.

AI can manage simple and repetitive requests. Humans can focus on:

Complex technical issues
Sensitive complaints
Security incidents
High-value customers
Unusual exceptions
Retention conversations

AI can also assist human agents by drafting replies, summarizing long conversations, translating messages, and retrieving relevant documentation.

The 70% Figure Needs Context

Not every company will automate 70% of its support volume.

The result depends on:

The quality of the knowledge base
The types of customer questions
Access to business systems
Escalation rules
Workflow design
How resolution is measured

A company with clear documentation and thousands of repetitive questions may achieve a high automation rate.

A company handling legal, medical, financial, or highly technical cases may require more human involvement.

The Biggest Challenge Is Knowledge

AI support agents are only as reliable as the information available to them.

Outdated articles, conflicting policies, and missing documentation can lead to incorrect answers.

This is why AI support projects often become knowledge management projects.

Companies must clean their documentation, remove duplicate information, define clear policies, and continuously review failed conversations.

Final Thoughts

AI is not quietly replacing customer support.

It is quietly automating the predictable parts of it.

Platforms like Zendesk, Intercom, and Freshworks are turning support bots into agents that can understand questions, retrieve information, complete actions, and escalate when necessary.

The goal should not be maximum automation.

The goal should be reliable resolution.

AI should handle repetitive work quickly, while humans remain responsible for the situations that require judgment, empathy, and accountability.

I kept skipping events alone. So I built Evento to fix that.

Nimesh Kulkarni — Sun, 07 Jun 2026 17:32:12 +0000

For a long time I had a habit of finding events I was genuinely excited about and then not going.
Not because I did not want to. Because my friends were busy. Or not interested. Or I just could not bring myself to walk into a room full of strangers alone.
Concerts. Hackathons. Local treks. Community meetups. I would find them, get excited, and then talk myself out of going because I had no one to go with.
At some point I asked myself a simple question. What if I could find someone else who is already going to that same event?
That is Evento.
It matches you with verified people going to the same events as you. Real profiles, verified users, no randomness. Whether you are introverted, shy, or just have a friend group that is always somehow busy on the exact weekend you want to do something, Evento is built for exactly that feeling.
I am not building this for some abstract user persona. I built it because I was tired of skipping things I actually wanted to do.
Launching in few days. If this sounds like something you have needed, the waitlist is open right now.
https://evento.n1m35h.in/waitlist

AI Companies Are Paying Millions for Your Old Reddit Posts. Here's Why That Should Concern You.

Nimesh Kulkarni — Sat, 06 Jun 2026 10:06:04 +0000

I am so tired of opening my code editor and seeing the same AI-generated dashboard. Same layout. Same gradient. Same components. It all looks like it came out of the same broken photocopier.

Here is what is actually going on.

Models trained on AI output degrade over time. Researchers call it model collapse. Every generation trained on synthetic data gets slightly worse than the last. Diversity drops. The weird, specific, human stuff disappears. Everything drifts toward a boring average.

By April 2025, over 74% of newly created webpages had AI-generated text in them. Stack Overflow drowned in AI answers overnight. Content farms switched to pure synthetic output the moment it became cheap enough.

The web is now mostly a mirror reflecting a mirror.

So what are OpenAI, Google, and Anthropic doing about it? They are going back to the old internet. Pre-2022. Before the flood. Google signed a $60 million per year deal with Reddit just to access your old posts. OpenAI did the same. Anthropic got sued for taking it without asking.

Your decade-old forum arguments and niche shitposts are now formally worth more than anything being written today. That is not an exaggeration. That is a business decision backed by hundreds of millions of dollars.

Every boilerplate component you copy from an AI, every SEO article generated in bulk, every "here is a step by step guide" that reads like nothing, is feeding a loop that makes the whole thing worse.

I do not have a clean solution to pitch at the end of this. I am just a developer who is bored and annoyed that genuine human output has become the scarce resource in a world drowning in content.

Write something real. Even if it is ugly.

Vite Just Got a Bigger Engine: Why VoidZero Joining Cloudflare Matters

Nimesh Kulkarni — Fri, 05 Jun 2026 12:33:31 +0000

If you build frontend apps in 2026, there is a good chance Vite is somewhere in your workflow. Maybe directly. Maybe through your framework. Maybe through a test runner, plugin, or starter kit you barely think about anymore.

That is why VoidZero joining Cloudflare is worth paying attention to.

VoidZero is the team behind Vite, Vitest, Rolldown, Oxc, and Vite+. Cloudflare says those projects will stay open source, vendor-agnostic, and community-driven. The interesting part is not “company acquires tooling team.” The interesting part is what this signals for the next phase of JavaScript tooling: faster, more integrated, and closer to production runtimes.

The JS toolchain is finally consolidating

For years, frontend tooling felt like a group project where everyone submitted a different build system at 11:59 PM.

Bundler here. Transpiler there. Test runner somewhere else. Dev server doing its own thing. Then your production runtime casually has different behavior because, of course, why not.

Vite changed the default expectation: local dev should feel instant, framework authors should get a solid foundation, and teams should not need a PhD in build configs just to ship a button.

VoidZero pushes that further with projects like:

Vitest for fast testing that feels native to Vite projects
Rolldown as a Rust-powered bundler direction for the ecosystem
Oxc for fast parsing, transforming, linting, and formatting foundations
Vite+ as a commercial layer around the open tooling

The vibe is clear: less glue code, fewer duplicated pipelines, faster feedback loops.

Why Cloudflare is an interesting home

Cloudflare has been leaning hard into developer infrastructure: Workers, Pages, D1, KV, R2, Durable Objects, Workflows, AI tooling, and local development around workerd.

The key developer pain here is simple: local dev should match production more closely.

Cloudflare’s Vite plugin already runs server code inside workerd, the same open-source runtime model behind Workers. That means you can test platform features locally without pretending everything is just Node.js.

That is a big deal. Ngl, “works locally, breaks in production” is one of the least funny recurring jokes in web dev.

What developers should actually do now

Do not rewrite your stack because of one announcement. That is how we summon yak-shaving demons.

Instead, make a small practical check:

npm create vite@latest
npm install -D vitest

If you are already on Vite, review whether your project still has old build/test glue that Vite-native tooling can replace. If you deploy to edge or serverless runtimes, check whether your framework has a runtime-aware Vite integration instead of relying on generic Node-only assumptions.

Also, keep an eye on Rolldown and Oxc. Not because you need to migrate today, but because the performance work happening there will likely show up through frameworks and tools before most app teams touch it directly.

The takeaway

VoidZero joining Cloudflare is not just frontend ecosystem gossip. It is another sign that the JavaScript toolchain is moving from “many separate tools wired together” toward “one faster pipeline from dev to deploy.”

For developers, the win is simple: fewer weird config layers, faster feedback, and local environments that behave more like production.

That is the kind of boring infrastructure improvement that quietly makes everyone ship better software. Lowkey, those are the best ones.

Kubernetes Dashboard Is Passing the Baton — Headlamp Is the Upgrade Path

Nimesh Kulkarni — Thu, 04 Jun 2026 12:34:18 +0000

Kubernetes Dashboard has been that classic “first window into the cluster” for a lot of devs. You install it, click around pods and services, and suddenly Kubernetes feels less like dark magic.

But the Kubernetes project is now clearly pointing users toward Headlamp as the modern replacement path. And honestly? That makes sense.

This is not just a UI swap. It is a small signal about where Kubernetes tooling is going: less “one built-in dashboard for everyone” and more extensible, safer, workflow-friendly interfaces around real cluster operations.

Why this shift matters

The old Dashboard solved a real onboarding problem: “What is running in my cluster?”

But production Kubernetes teams need more than that:

clearer visibility across namespaces and workloads
safer access patterns for different users
plugin-friendly workflows
better alignment with modern cluster operations
a UI that can grow without becoming a giant one-size-fits-all panel

That is where Headlamp is interesting. It is a CNCF project built as a Kubernetes UI that can be extended, embedded into workflows, and used across different cluster setups.

Lowkey, this is exactly what Kubernetes tooling needed. Not another shiny control panel. A better foundation.

The practical developer takeaway

If you are still using Kubernetes Dashboard for learning or lightweight inspection, you do not need to panic-migrate today.

But if you are building team workflows, internal platforms, or anything close to production, start treating Headlamp as the thing to evaluate next.

A simple migration mindset:

Learning cluster / quick inspection
  -> Dashboard is okay for now

Team platform / shared cluster UI
  -> Evaluate Headlamp

Production ops workflow
  -> Combine UI access with RBAC, audit logs, and CLI/GitOps flows

The important part is not “UI vs CLI.” Real teams use both. The UI helps people understand and inspect. The CLI and GitOps pipelines keep changes controlled and repeatable.

Do not skip the boring security bits

Any Kubernetes UI is only as safe as the permissions behind it.

Before rolling one out, check:

Does every user have the minimum RBAC they need?
Are admin actions limited to the right people?
Is access behind SSO or your normal identity layer?
Can you audit who viewed or changed what?
Are destructive actions restricted?

Because a beautiful cluster UI with overpowered permissions is just a very polished footgun. Fr.

My take

The Dashboard-to-Headlamp transition is a healthy move. Kubernetes is mature enough now that the default UX should not just be “show me objects.” It should support real operational workflows.

For developers, the move is simple: keep using visual tools to learn faster, but design production access like an engineering system, not a convenience shortcut.

Actionable takeaway: if your team exposes a Kubernetes UI, review the RBAC model this week and test Headlamp in a non-prod cluster before the migration becomes urgent.

Agent-Native Desktops Are Coming: Code Review Is the First Workflow to Fix

Nimesh Kulkarni — Wed, 03 Jun 2026 12:32:46 +0000

AI coding tools are moving from “chat box next to your editor” to something much more serious: agent-native developer environments.

GitHub just pushed this direction hard with the Copilot app and desktop-style agent workflow announcements. OpenAI is also framing Codex as a broader productivity layer, not just a coding prompt. The signal is pretty clear: agents are becoming part of the work surface, not a side quest.

That sounds futuristic, but for most teams the first real place to care is boring and practical: code review.

The problem is not generation anymore

Generating code is no longer the impressive part. Most teams already know an AI assistant can draft a component, write tests, explain a stack trace, or refactor a file.

The messy part is everything after generation:

Which agent changed what?
Did it understand the existing architecture?
Are the tests meaningful or just green-looking?
Is the diff safe enough to merge?
Who owns the final decision?

Ngl, this is where “AI boosted my productivity” can quietly become “AI created a review queue I do not trust.”

Agent-native means workflow-native

An agent-native desktop should not just be a nicer chat UI. The useful version gives agents a real workflow:

Task context: issue, branch, constraints, and acceptance criteria
Workspace access: repo, terminal, tests, docs, and dependency graph
Change tracking: clean diffs, summaries, and decisions made
Review gates: lint, tests, security checks, human approval
Memory boundaries: what the agent can reuse, forget, or escalate

That is the difference between “vibe coding with a powerful autocomplete” and “delegating a small engineering task with guardrails.”

Start with the review loop

If your team wants to adopt agentic coding without chaos, do not begin by letting agents ship bigger features. Begin by making reviews easier to trust.

A practical setup can be simple:

agent_review:
required:
- summarize_diff
- list_risk_areas
- run_tests
- flag_security_sensitive_files
- explain_remaining_uncertainty
human_decision: required

The magic is not the YAML. The magic is forcing the agent to show its work before a human approves the merge.

What developers should watch next

The next wave of dev tools will compete on orchestration, not just model quality. The best tools will help us manage multiple small agents, compare their changes, rerun checks, and keep review context visible.

That is a big deal because developers do not need more random AI output. We need tighter loops, cleaner diffs, and less context switching.

Takeaway

Agent-native desktops are worth watching, but do not wait for the perfect tool. Start designing your workflow like agents are junior teammates: give them scoped tasks, require evidence, and keep humans in charge of merge decisions.

That is how AI coding becomes useful in real teams: not louder, just safer and smoother.

Stop Shipping Blob Tokens: OIDC Is the Cleaner Deploy Secret

Nimesh Kulkarni — Tue, 02 Jun 2026 12:34:29 +0000

Ngl, one of the easiest ways to make a deployment pipeline sketchy is to give it a long-lived storage token and hope nobody ever leaks it.

That pattern is still everywhere: CI uploads static assets, build artifacts, screenshots, or user-facing files to some blob bucket, and the workflow stores a token in secrets.BLOB_TOKEN forever.

It works.

It also quietly creates a key that can outlive the job, the branch, the intern who created it, and sometimes the entire project. Big yikes.

The better pattern that keeps showing up across modern platforms is OIDC-based deploy auth: your CI job proves who it is, asks the provider for short-lived access, does the upload, and disappears.

The old flow

Most teams start here:

- name: Upload assets
  env:
    BLOB_TOKEN: ${{ secrets.BLOB_TOKEN }}
  run: npm run upload-assets

Simple, but the risk is obvious:

the token sits in your repo/org secrets forever
rotation becomes another chore nobody owns
leaked logs, compromised runners, or copied workflows can turn into real access
every workflow tends to get more permission than it actually needs

For a side project, maybe that feels acceptable. For production pipelines, it becomes tech debt with security vibes.

The OIDC flow

With OIDC, GitHub Actions or another CI provider can request an identity token for the current job. The cloud/storage platform verifies claims like repo, branch, environment, and workflow, then issues temporary credentials.

The workflow shape becomes closer to this:

permissions:
  id-token: write
  contents: read

jobs:
  upload:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Upload assets with short-lived auth
        run: npm run upload-assets

The actual provider setup differs, but the idea is the same: trust the workload identity, not a copied secret string.

This is why recent platform updates around OIDC for blob/storage workflows are worth paying attention to. They are not just “nice auth features.” They are a signal that deployment secrets are moving from static keys to verified, short-lived identity.

Why developers should care

This is not only a security-team thing.

OIDC makes day-to-day engineering cleaner:

fewer secrets to create, share, rotate, and audit
safer preview deployments from protected branches or environments
tighter permissions per repo/workflow instead of one mega-token
less panic when someone accidentally prints an env var in CI

Tbh, the best security improvements are the ones that remove a thing you had to remember. OIDC does exactly that.

A quick migration checklist

If your build uploads anything to blob storage, artifact storage, package registries, or cloud buckets, check this:

Is the workflow using a long-lived token?
Does the provider support OIDC or workload identity federation?
Can access be scoped by repo, branch, workflow, or environment?
Can the token be removed after the OIDC path works?

Do not migrate everything in one heroic PR. Pick one low-risk upload job, move it to OIDC, and document the pattern for the next workflow.

The takeaway

Long-lived deploy tokens were convenient when CI/CD was simpler.

But now that pipelines publish assets, trigger releases, talk to AI services, and touch production infra, “just store a token” is starting to look outdated.

If a platform gives you OIDC for deploy-time storage access, use it. Your future self will have one less secret to babysit — and that is a clean W.

Multi-Agent Code Reviews Need Pipelines, Not Vibes

Nimesh Kulkarni — Mon, 01 Jun 2026 12:36:23 +0000

Most teams are about to hit the same AI coding problem: one agent can write a lot of code, but it cannot be the only judge of its own work.

That is where multi-agent review starts to matter.

The next useful dev workflow is not “ask the model to be careful.” It is a pipeline where different agents own different checks, and boring deterministic tools still do the boring deterministic work.

The single-agent trap

A coding agent can implement a feature, update tests, and explain the diff. That is already powerful.

But if the same agent writes the code, reviews the architecture, checks security, decides whether tests are enough, and summarizes the risk, you are basically asking one intern to approve their own pull request.

That is not a workflow. That is optimism with a CLI.

A better pattern

Think of AI agents like specialized reviewers in your engineering loop:

Implementation agent: writes the first version of the change.
Test agent: looks for missing cases, edge conditions, and regression gaps.
Security agent: checks auth paths, secrets, injection risks, unsafe dependencies, and data exposure.
Architecture agent: watches for coupling, weird abstractions, and changes that fight the existing system.
Summary agent: turns the mess into one clean PR comment humans can actually use.

The point is not to add AI everywhere. The point is to stop pretending one model pass is enough.

CI is the control plane

The cleanest place to run this is still CI.

A practical pull request flow can look like this:

Run lint, type checks, unit tests, and build checks.
Trigger focused AI reviewers only after the deterministic checks finish.
Give each reviewer a narrow prompt and a narrow permission scope.
Collect findings into one review summary.
Escalate only the risky or uncertain parts to a human.

This keeps the workflow grounded. Traditional tools catch facts. Agents catch judgment-heavy issues.

MCP makes this easier, but also riskier

With MCP and similar tool layers, agents can access GitHub, CI logs, docs, issue trackers, observability tools, and internal systems through a standard interface.

That is exactly why the pattern is useful.

It is also exactly why permissions matter.

Start read-only. Log every tool call. Scope access to specific repos and workflows. Do not give an AI reviewer write access just because the demo looked clean. If it can comment on a PR, that is already useful. If it can merge, deploy, or mutate infrastructure, that needs a much higher bar.

The real upgrade

The future of AI coding is not one giant prompt that does everything.

It is smaller agents, clearer jobs, shared context, deterministic checks, and human attention saved for the decisions that actually need taste.

Multi-agent code review is not about replacing review culture.

It is about making review culture scale when AI starts generating more code than humans can comfortably inspect line by line.

Markdown Is Becoming the AI App Interface

Nimesh Kulkarni — Sun, 31 May 2026 12:35:12 +0000

A quiet developer pattern is getting louder: Markdown is becoming the interface layer for AI apps.

Microsoft's markitdown project has been trending because it solves a boring problem that keeps showing up in real products: how do you turn PDFs, Word files, slide decks, spreadsheets, and HTML pages into something an AI system can actually use?

The answer is not glamorous. Convert the mess into Markdown.

That sounds too simple, but it works because AI apps do not fail only at the model layer. They fail when the input is messy, hidden, lossy, or impossible to inspect.

Why Markdown keeps winning

Markdown has a boring superpower: everyone can read it.

A developer can open it in a terminal. Git can diff it. Static sites can render it. LLMs can follow headings, lists, code blocks, and links without needing a custom parser for every file type.

That makes Markdown a solid handoff format between real-world documents and AI workflows.

PDF / DOCX / PPTX / HTML
        ↓
Markdown
        ↓
cleanup + validation
        ↓
search, RAG, summaries, agents, docs

The important part is not "Markdown is cool." The important part is that Markdown gives your pipeline a visible middle layer.

If the output is bad, you can inspect the Markdown and see where the context broke.

The use case that matters

Imagine an internal AI assistant for a support or engineering team.

The source material is never clean. There are old onboarding docs, customer PDFs, policy pages, architecture notes, product specs, and random exports from tools nobody wants to maintain.

Without a common format, every file type becomes a separate problem.

With Markdown, the flow is simpler:

markitdown product-spec.pdf > product-spec.md

Then the app can clean it, split it by headings, index it, summarize it, or pass selected sections into an agent.

That is the real win. Markdown becomes the boring contract between messy documents and useful AI behavior.

Where this helps right now

This pattern is useful when teams want to:

turn support docs into searchable knowledge
feed architecture notes into coding agents
migrate old documents into a docs site
build lightweight internal knowledge bases
keep AI context auditable instead of hiding it inside a black-box parser

The last point matters. When an AI answer is wrong, the team needs to debug the context, not just blame the model.

If the context is Markdown, you can read it. You can diff it. You can fix it.

The trap

Conversion is not magic.

Tables can break. Scanned PDFs may need OCR. Images can disappear. Slide decks can lose structure. Footnotes can land in weird places.

So the pipeline should not be:

convert -> trust blindly -> ship

It should be:

convert -> validate -> normalize -> use

A small validation step saves a lot of weird AI behavior later.

Takeaway

Markdown is becoming more than a writing format. It is becoming a practical interface for AI apps.

Before adding another vector database, agent framework, or prompt trick, check the boring thing first: is the input clean, readable, and inspectable?

Most AI products get better when the context gets better. Markdown is one of the simplest ways to make that happen.

Your AI Coding Agent Does Not Need a Bigger Prompt

Nimesh Kulkarni — Sat, 30 May 2026 19:40:39 +0000

AI coding agents are getting better, but the annoying part has not disappeared.

You still paste the same project details. You still explain the same folder structure. You still remind the agent which framework version you use, where the issue came from, and what “done” means in your repo.

That is not a model problem. That is a context problem.

The next useful shift for developers is simple: stop trying to make one giant perfect prompt. Build a small context system around the agent.

Give the agent the things a real teammate would ask for before touching code:

the issue or task source
the relevant docs
the repo conventions
the error logs
the recent decisions
the tests that prove the change works

This is why context engineering and MCP are becoming a big deal. MCP gives agents a standard way to fetch tools, files, docs, tickets, databases, and workflows instead of forcing developers to paste everything manually.

The win is not magic. It is less repeated explanation.

A good agent setup should feel boring in the best way. The agent reads the issue, checks the right files, pulls current docs, makes a small change, runs the test, and reports what happened. You still review the work, but you are no longer acting like a human clipboard.

The mistake is giving the agent every possible tool and hoping it figures life out. That just creates noise. Better setup:

start with one repo
add only the context sources that repo actually needs
prefer read-only access first
write rules for when each tool should be used
keep memory small and durable
verify outputs with tests, links, or logs

The best AI coding workflow in 2026 is not “prompt harder.”

It is giving the agent a clean bench: current docs, scoped tools, project rules, memory that does not rot, and a verification loop.

Bigger prompts make agents look busy.

Better context makes them useful.

Your AI Agent Should Text You First

Nimesh Kulkarni — Sat, 30 May 2026 18:48:07 +0000

This is a submission for the Hermes Agent Challenge: Write About Hermes Agent.

Most AI assistants wait around like interns who lost the Slack invite.

You open a tab. You type a prompt. You explain the same project again. You paste the same links again. Then you spend half the afternoon checking whether the answer is real.

That was fine when AI was a fancy autocomplete box.

It is not fine for agents.

The most interesting Hermes Agent use case is not "chatbot, but with tools." It is a small always-on chief of staff that lives on your server, watches the boring parts of your life, remembers how you like things done, and texts you when there is something worth seeing.

Not Jarvis. Not a sci-fi butler. More like a very caffeinated operations person who never sleeps and occasionally judges your TODO list.

The winning use case: an agent that texts first

The trend in 2026 is obvious: agents are moving from short chat sessions to long-running workflows.

Developers are using coding agents to inspect repos, run tests, open PRs, and iterate for minutes or hours. Teams are wiring tools through MCP instead of writing one-off integrations for every model. Personal agent users care more about memory than raw prompt cleverness because they are tired of re-explaining their life to a rectangle.

Hermes sits right in that intersection:

It can run on your own machine, VPS, container, or cloud backend.
It has a messaging gateway, so the agent can live where you already talk: Telegram, Discord, Slack, WhatsApp, email, and more.
It has persistent memory, session search, and skills, so it can improve instead of starting from zero every morning.
It has cron jobs and webhooks, so it can act without waiting for you to remember that you forgot something.
It can use tools, MCP servers, terminal commands, files, browsers, image generation, TTS, and subagents.

That combination changes the shape of the product.

A normal assistant answers:

"Summarize this news article."

A proactive Hermes workflow says:

"Every morning, check the agent ecosystem, verify the useful stories, ignore duplicates, write a short brief in my style, generate a cover card, post it to Telegram, and tell me what changed from yesterday. If the workflow breaks, explain exactly where."

That is a different animal.

The 5-step loop

The best Hermes workflow I would build for the challenge is simple:

Watch: news feeds, GitHub repos, issues, inboxes, calendars, RSS, dashboards, or whatever system currently makes you say "I'll check that later".
Verify: fetch the source, compare multiple references, avoid hallucinated summaries, and keep receipts.
Produce: write the brief, generate the diagram, draft the PR, create the issue, update the note, or prepare the message.
Report: send the final result back through the platform where the human actually is.
Learn: save the workflow as a skill when it works, then reuse that procedure next time.

The last step is the part people underestimate.

A tool-using agent is useful. A tool-using agent that writes down what worked is dangerous in the best way. The first run is messy. The fifth run starts to feel like you hired someone.

Why Hermes is a good fit for this

A lot of agent frameworks can call tools. That is table stakes now.

Hermes gets interesting because it treats the agent less like a browser tab and more like a resident process.

1. The gateway makes it reachable

The agent does not need to be trapped inside your terminal. You can talk to it from Telegram while walking, Discord while shipping, or the CLI when you are deep in a repo.

That sounds cosmetic until you try it.

The best automation is the one you can trigger at the moment you think of it. If I remember a blog idea while making coffee, I do not want to open my laptop, find the right repo, activate a virtualenv, and perform a tiny ceremony. I want to send a voice note and move on with my life.

2. Cron makes it proactive

Cron is boring, which is why it wins.

An agent that waits for prompts becomes another tab to manage. An agent with scheduled jobs becomes infrastructure.

Examples:

"Every weekday at 9 AM, brief me on AI agent news."
"Every Friday, check my open-source issues and suggest one realistic contribution."
"Every night, scan my notes and generate tomorrow's priority list."
"Every morning, check whether my blog pipeline ran and tell me if it did not."

Yes, that last one is personal. No, I will not be taking questions.

3. Memory prevents Groundhog Day

Without memory, agents become expensive goldfish.

You say:

"Use short wording. Prefer IST times. My DEV.to handle is this. My images live in that GitHub repo. Do not restart the gateway while another agent is working."

Then, next week, the agent asks again.

At that point the AI has not saved time. It has merely outsourced your irritation to a GPU.

Hermes has persistent user memory, regular session history, and procedural skills. Those are different kinds of context:

User memory: durable preferences and facts.
Session search: what happened in past conversations.
Skills: reusable procedures for doing a class of work.

That separation matters. "Nimesh prefers IST times" is memory. "How to publish a DEV.to article with hosted images" is a skill. "We fixed yesterday's cover image" is session history.

When those get mixed together, the agent becomes messy. When they are separated, it starts to feel senior.

4. Skills make good work repeatable

Skills are the sleeper feature.

Most people think the hard part is getting an agent to complete one task. That is only half the problem. The real win is making sure the agent does not need the same painful steering next time.

A good skill is not a motivational quote stuffed into memory. It is a playbook:

when to use it
which tools to call
which files or APIs matter
what can go wrong
how to verify the result

That is basically how senior people work too. They do not remember every detail. They remember the shape of the problem, the traps, and the checklist that prevents clown behavior.

A concrete build: the personal signal desk

If I were building one Hermes project to impress judges, I would build this:

The Personal Signal Desk: an always-on Hermes workflow that watches your chosen domain, finds high-signal updates, creates a short daily briefing, generates simple visuals, posts it to your preferred chat, and improves its own sourcing rules over time.

For a developer, it could watch:

GitHub trending repos in AI agents
MCP server releases
relevant DEV posts
Hacker News discussions
docs changes from tools you use
your own repos and issues

For a founder, it could watch:

competitor launches
pricing page changes
job postings
funding announcements
customer complaints on Reddit
product mentions on social channels

For a student, it could watch:

internship openings
research papers
hackathons
scholarship deadlines
university notices
your own study plan

Same architecture. Different sources. Different skills.

The agent should not dump fifty links. That is not intelligence. That is a link landfill.

It should come back with five things:

what changed
why it matters
source links
what action to take
what it learned for tomorrow

That last line is where Hermes earns its keep.

What the workflow looks like in practice

Here is the boring-but-real version:

08:55  Cron wakes Hermes
08:56  Hermes searches configured sources
08:58  It fetches original pages, not just search snippets
09:01  It removes duplicates and weak stories
09:03  It writes a short brief in the user's style
09:04  It generates a visual summary card
09:05  It posts to Telegram with source links
09:06  It saves what worked as a skill update if the run revealed a better process

The funny thing about useful agents is that the final demo looks almost too simple.

A message arrives.

That is it.

But underneath that message is search, validation, memory, tool use, scheduled execution, file handling, maybe image generation, maybe TTS, and a bunch of tiny verification steps nobody wants to do manually.

This is why I like the "chief of staff" framing. A chief of staff does not exist to look magical. They exist to reduce chaos.

The meme version

Because every agent blog needs one tiny unserious diagram or the build gods get angry:

The joke works because it is only half a joke.

A proactive agent can absolutely become annoying if you let it spray notifications everywhere. The trick is to make it earn interruption rights.

My rule would be:

If Hermes messages me first, the message must either save time, prevent a mistake, or show completed work.

No vibes-only pings. No "just checking in" spam. No fake productivity confetti.

Receipts or silence.

The senior-person checklist

If you want this workflow to survive past the demo, design it like production software.

Start with one narrow job

Do not build "my entire life OS" on day one unless you enjoy debugging your own ambition.

Pick one job:

daily AI brief
weekly open-source contribution scout
blog publishing assistant
inbox triage
release monitor
meeting follow-up drafter

Make that boringly reliable. Then add the next thing.

Separate memory from logs

Do not save every random event as durable memory. That is how your agent becomes a haunted attic.

Save durable facts. Keep task history in sessions. Put procedures into skills.

Verify before publishing

If the workflow posts publicly, make verification part of the workflow.

For a blog pipeline, that means:

raw image URLs return 200
the article API returns success
the public page loads
tags are correct
the cover has no accidental text if that is the visual rule

Yes, this is tedious. That is exactly why the agent should do it.

Keep humans in the loop for risky actions

Autonomy is not the same as recklessness.

Let Hermes draft, check, summarize, open PRs, and prepare posts. Be more careful with destructive commands, money movement, production deploys, external emails, and anything that can embarrass you at scale.

The best agent setup is not "YOLO everything." It is scoped trust.

Make the output easy to judge

Every proactive workflow should answer:

What did you do?
What changed?
What sources did you use?
What failed?
What should I do next?

If the agent cannot explain itself, it is not done. It is just confident.

Why this can win a challenge

The Hermes Agent Challenge write track is judged on clarity, depth, originality, practical value, and writing quality.

A proactive chief-of-staff workflow hits all five because it is not abstract. It shows what makes Hermes different from a normal assistant:

Clarity: the loop is easy to understand.
Depth: it uses memory, skills, cron, tools, and gateway together.
Originality: the agent is not just answering prompts; it is operating over time.
Practical value: anyone can adapt the pattern to their own domain.
Writing quality: hopefully this post has not sounded like a toaster explaining synergy.

The broader point is this:

AI agents become useful when they move from conversation to operations.

A conversation is "help me think."

Operations is "watch this, handle the routine parts, wake me up when it matters, and get better at the job."

Hermes is built for that second category.

Final thought

The future of personal AI probably will not feel like one giant chatbot that knows everything.

It will feel like a set of small dependable loops:

one loop watches your work
one loop watches your health
one loop watches your projects
one loop watches your learning
one loop watches your public presence

Hermes is interesting because it gives those loops a home. A place to run. A memory to grow into. A skill library to improve. A way to reach you without making you open another tab.

That is the actual unlock.

Not an assistant that waits politely.

An agent that texts first, with receipts.