DEV Community: The AI producer

A real-time global intel dashboard with zero backend

The AI producer — Sat, 11 Jul 2026 03:43:56 +0000

Six free APIs replaced an entire dashboard backend. Here's the trick.

Hosted "world monitor" products run fleets of edge functions, Redis clusters, and paid API tiers to show you a map of what's happening. SITUROOM is my counter-bet: earthquakes (USGS), natural hazards (NASA EONET), conflict news (GDELT), crypto (CoinGecko), FX (Frankfurter), and Hacker News are all keyless and CORS-friendly. So the browser does everything.

The result is one static folder. Serve it with python3 -m http.server. Or fork it and flip on GitHub Pages.

Open the live demo: https://nguyenminhduc9988.github.io/situroom/

What you get, all free and MIT:

A dark ops-style canvas world map: pulsing quake markers, hazard layers, tension heat. Vendored d3 — the app loads nothing from any CDN.
A per-country tension index that is not a black box. The formula is ~30 lines of commented client-side code. Open devtools and recompute it. 44 offline unit tests pin it down.
Honest failure modes. Each source degrades independently: live fetch, then last-good cache (STALE chip), then a committed real-data snapshot (SNAPSHOT chip). During launch testing GDELT rate-limited my IP. The board kept working and said so.
A free local MCP server. Zero-dependency Python, stdio. Your agent gets get_quakes, get_tension, get_headlines — the same feeds the dashboard renders. No API key. The Python tension port is byte-identical to the JS original.
Optional AI sitrep, bring-your-own-key. Any OpenAI-compatible endpoint or local Ollama. Keys never leave localStorage.
PWA. Second visit serves the whole shell from the service worker: zero bytes for the app.

Six sources today. Adding one is a pure normalize function, a unit test against a captured payload, and a fetch that returns null on failure. The GitHub link is in the demo's top bar — PRs welcome.

I built repolens — X-ray any GitHub repo into one interactive HTML file

The AI producer — Fri, 10 Jul 2026 04:30:42 +0000

TL;DR: paste any public GitHub repo into lens.bigwinner.work and get an interactive architecture map — dependency graph, blast radius, health grade, security scan. Or run uvx repolens-kit owner/repo locally. Open source, MIT.

The problem

Every time I open an unfamiliar codebase I ask the same four questions:

Where does the mass of this thing live?
Which files are the load-bearing walls?
If I change this file, what breaks?
Is there anything scary committed in here?

You can answer all of these by reading code for a week. I wanted them answered in five seconds.

What repolens does

One command — repolens owner/repo — produces a single self-contained HTML file (D3 inlined, zero external requests, safe to attach to a PR):

🕸️ Interactive dependency graph — every file is a node, sized by lines of code. Color by folder, layer, git churn, or blast radius. Collision-aware labels keep it readable at 1,000+ nodes.
💥 Blast radius — click any file and see exactly how many files transitively depend on it, with a severity meter. flask/__init__.py ripples to 42% of the codebase; now you know to test hard.
🏥 Health grade (A–F) — cycles, coupling, oversized files, security findings. The penalty breakdown is shown, so it's never a black-box number.
🔐 Security scan — committed keys/tokens, SQL built by string-glue, eval/exec, disabled TLS verification. Findings in test paths are auto-demoted.
🔥 Hotspots & ownership — per-file commit churn and top contributors mined from git history.

It resolves imports to files inside the repo to build a real graph — Python, JS/TS, Java, Kotlin, Go, Rust, C/C++, C#, PHP, Ruby, Elixir and ~30 more. Fully-qualified imports (com.acme.core.Engine) resolve by path-suffix matching with language-family guards, so a Java import never creates a fake edge to a Python file.

Zero-clone analysis

The hosted version never writes a repo to disk: it streams GitHub's tarball through tarfile in stream mode, filtering and size-capping files as the bytes arrive. A 100-file repo analyzes in about a second.

Try it

Browser, no install: lens.bigwinner.work — free, no signup
Terminal: uvx repolens-kit owner/repo (or pip install repolens-kit)
Source (MIT): github.com/nguyenminhduc9988/repolens

Honest limitations: import-based analysis can't see same-package Java references (no import statement to parse), and the hosted tier caps repos at 3,000 files — the CLI has no limits.

Feedback and issues very welcome. What would you want an architecture X-ray to show?

7 AI Prompts I Actually Use to Run My Solo Dev Business (Copy Them Free)

The AI producer — Tue, 07 Jul 2026 12:14:42 +0000

I run a small software business by myself — no co-founder, no team. The thing that keeps it alive isn't a clever framework or a hot stack. It's a short list of AI prompts I've written and rewritten until they actually save me time instead of burning it.

Here are seven I use every single week. The exact text is in each block. Copy it, change the bracketed parts, and make it yours.

1. The "standup for one" prompt (mornings)

I open my editor before I've fully woken up. This prompt turns a vague brain-dump into a focused plan so I don't spend 30 minutes deciding what to do first.

Act as my standup partner. Here are yesterday's outcomes and today's tasks: [paste].

1. Identify the single highest-leverage task today and why it matters most.
2. Flag any task carried over 2+ days and tell me whether to cut it or commit to it.
3. Give me a 3-step plan ordered by energy cost — hard thing first.
4. End with one question I should be able to answer by end of day.

2. The Friday weekly reset

Friday afternoons are for closing loops. Without this, Monday me walks into a half-finished mess.

Act as my weekly reviewer. Here's what I shipped, what slipped, and how the week felt: [paste].

1. Surface one decision that worked and one that didn't.
2. Find a recurring time-sink to automate, delegate, or delete next week.
3. Draft 3 goals for next week that ladder to a 90-day aim.
4. Tell me one thing to stop doing.

3. The pull-request description writer

Writing the PR is often longer than writing the code. This handles the boilerplate so reviewers get a clean summary.

Here is my diff: [paste].

Write a pull-request description with:
- "What" — one-line summary a reviewer can skim.
- "Why" — the user or business reason, not the implementation reason.
- "How to test" — exact reproduction steps.
- "Risk" — what could break and how to roll back.
Keep it under 150 words. No marketing tone.

4. The commit-message generator

Messy commit history makes future-you hate past-you.

Given these staged changes: [paste].

Write 3-5 conventional commits (type(scope): subject). Group related changes.
Each subject line <= 50 chars, imperative mood. Add a body only if the "why" is non-obvious.

5. The debugging partner

This one stops me from going down rabbit holes. It forces me to state my assumption out loud — which is usually where the bug lives.

I'm debugging this. Here is the code and the unexpected behavior: [paste].

Before suggesting fixes:
1. Restate my assumption about how the code works.
2. Point out the single line where that assumption is weakest.
3. Give me one experiment that would confirm or rule out that line as the cause.
Do not hand me a rewritten solution yet.

6. The cold client email

One client pays for a year of side-project hosting. This keeps outreach short and human.

Write a cold email to [name], who works on [their project]. I noticed [specific real thing].
Constraints:
- Under 90 words, plain text, no buzzwords.
- Lead with something useful to THEM, not me.
- One soft question, not a hard ask.
- Sound like a person, not a template.

7. The "decide already" summarizer

When I've collected too many notes and can't see the decision, this collapses it.

Here are my scattered notes on a decision: [paste].

1. State the actual decision in one sentence (often I haven't even named it).
2. List the 2-3 real options.
3. For each, the single strongest argument for and against.
4. Tell me which option a rational outsider would pick and why.
Do not hedge. Pick.

The honest part

These seven are the ones that stuck. I keep the full set — morning routines, weekly resets, content, client follow-ups, the unglamorous operational stuff — in a free starter pack of 10 prompts if you want to skip the writing.

And if you want the whole library I work from, it's a 68-prompt vault built around running a one-person business without burning out. No subscription — one-time, and the free pack covers the most useful bits if you'd rather not pay.

Either way: steal the seven above first. The point isn't the pack, it's having prompts tuned enough that you actually use them.

7 Open-Source Python Tools I Built While Automating My Own Work (All Zero-Dependency)

The AI producer — Mon, 06 Jul 2026 23:17:46 +0000

I run a small automation setup that handles content, scheduling, and browser tasks for me. Over a few months I kept re-implementing the same building blocks: scoring prompts before I ship them, guarding tool calls so a bad input can't wreck something, compressing context when it gets too long, and remembering things between runs.

So I extracted them into standalone packages and open-sourced all of them. Every one is zero-dependency (Python 3.11+ stdlib only) — pip install and go, no transitive dependency hell.

Here's what each does, with a real code snippet pulled from the repo.

1. `prompt-bench` — score and A/B test prompts

The problem: I'd tweak a prompt, ship it, and only discover weeks later it was worse. Now I score prompts on 12 dimensions before they touch production.

from prompt_bench import PromptScorer

scorer = PromptScorer()
result = scorer.score("You are a helpful assistant. Summarize the article.")

print(f"Overall: {result.overall_quality}")
for suggestion in result.suggestions:
    print(f"  - {suggestion}")

It also A/B-tests two variants and picks a winner with a per-dimension breakdown — useful when you're arguing with yourself over whether to add an example.

🔗 github.com/nguyenminhduc9988/prompt-bench

2. `hermes-tool-guard` — stop dangerous tool calls

The moment you let an LLM call tools (read_file, run_shell, http_get), you need a guard. This one validates inputs, rate-limits per tool, and blocks shell injection, path traversal, and SSRF before the call runs.

from tool_guard import ToolGuard

guard = ToolGuard(default_rate_limit=30)
result = guard.check_call("read_file", {"path": "/tmp/data.txt"})

if not result.all_safe:
    print(result.blocked_patterns)

Token-bucket rate limiting per tool, plus a rules engine with allow/deny/sanitize/log actions.

🔗 github.com/nguyenminhduc9988/hermes-tool-guard

3. `agent-loop-lite` — the Plan-Act-Observe loop

The core agentic pattern as a tiny state machine: plan → execute tools → observe → replan or finish. Bring your own LLM; the loop enforces transitions, manages a tool registry, and fires hooks at every step.

from agent_loop import AgentLoop, LoopState

def search_web(query: str) -> str:
    return f"Results for: {query}"

def save_data(data: str, filename: str) -> str:
    return f"Saved to {filename}"

loop = AgentLoop(
    tools=[
        ("search_web", search_web, {"type": "object", "properties": {"query": {"type": "string"}}}),
        ("save_data", save_data, None),
    ],
    max_iterations=10,
)

result = loop.run("Research AI trends and save a summary")
print(f"Status: {result.status.value}, Iterations: {result.iterations}")

Hooks (on_plan, on_execute, on_observe) make it easy to emit JSON logs of every step.

🔗 github.com/nguyenminhduc9988/agent-loop-lite

4. `mcp-wrap` — turn any REST API into an MCP server

Model Context Protocol is becoming the standard for handing tools to LLMs. Hand-writing a server per API is tedious — mcp-wrap wraps a REST endpoint into an MCP server in minutes.

🔗 github.com/nguyenminhduc9988/mcp-wrap

5. `context-window` — compress long conversations

When a conversation outgrows the context window, this manager compresses older turns intelligently instead of just truncating them — recent context stays verbatim, the rest gets summarized.

🔗 github.com/nguyenminhduc9988/context-window

6. `signet-lite` — persistent memory for CLI agents

SQLite + FTS5 full-text-search memory for command-line agents. Your agent remembers what it did last run without standing up a database server.

🔗 github.com/nguyenminhduc9988/signet-lite

7. `agent-memory` — multi-layer memory system

In-memory + file-based memory with multiple layers (short-term and long-term), for agents that need both fast recall and durable storage.

🔗 github.com/nguyenminhduc9988/agent-memory

Why zero dependencies?

Every package uses only the Python standard library. That gives you:

No version conflicts — install alongside anything
Fast cold starts — nothing to import-resolve
Auditable — you can read the whole codebase in an afternoon
No supply-chain risk — no transitive packages to vet

The trade-off is writing more yourself (I reimplemented a token bucket, an FTS5 wrapper, a JSON-schema validator). For infra-style tools like these, that trade is worth it.

What's next

All seven are MIT-licensed. If you're building agents and want to compare notes, I publish more of this at bigwinner.work — open-source tools and the occasional write-up.

If any of these are useful, a GitHub star genuinely tells me what to build next.

All repos live at github.com/nguyenminhduc9988.

7 Free Browser APIs You're Probably Not Using (But Should Be)

The AI producer — Fri, 03 Jul 2026 16:04:09 +0000

Most developers reach for libraries the moment they need clipboard access, notifications, or file handling. But modern browsers have built-in APIs for all of these — zero dependencies, zero bundle size.

Here are seven browser APIs that can replace common libraries in your projects.

1. The Clipboard API

Instead of importing clipboard libraries, use navigator.clipboard:

// Copy text to clipboard
await navigator.clipboard.writeText('Hello, world!');

// Read from clipboard
const text = await navigator.clipboard.readText();

Works in all modern browsers. Requires a secure context (HTTPS or localhost).

2. The Intersection Observer API

This replaced scroll-event-based lazy loading and "is element visible?" logic. Use it for infinite scroll, reveal animations, or analytics:

const observer = new IntersectionObserver((entries) => {
  entries.forEach(entry => {
    if (entry.isIntersecting) {
      entry.target.classList.add('visible');
    }
  });
}, { threshold: 0.1 });

document.querySelectorAll('.animate-on-scroll').forEach(el => {
  observer.observe(el);
});

No scroll listeners. No requestAnimationFrame. The browser handles it efficiently.

3. The Notification API

Push notifications without a service worker (for simple use cases):

if (Notification.permission === 'granted') {
  new Notification('Task Complete', {
    body: 'Your build finished in 12 seconds.',
    icon: '/favicon.ico'
  });
} else if (Notification.permission !== 'denied') {
  Notification.requestPermission();
}

Great for task timers, build tools, or admin dashboards.

4. The File System Access API

This lets users pick files or directories and gives you read/write access. It replaces <input type="file"> for more complex use cases:

const [fileHandle] = await window.showOpenFilePicker({
  types: [{ description: 'JSON', accept: { 'application/json': ['.json'] } }]
});
const file = await fileHandle.getFile();
const data = await file.text();

You can also write back with fileHandle.createWritable().

5. The Screen Wake Lock API

Prevent the screen from dimming during long-running processes (presentations, timers, dashboards):

const wakeLock = await navigator.wakeLock.request('screen');
// Later: wakeLock.release();

Useful for presentation apps, Pomodoro timers, or monitoring dashboards.

6. The Vibration API

Haptic feedback for mobile web apps:

// Short vibration (common pattern)
navigator.vibrate(100);

// Pattern: vibrate, pause, vibrate
navigator.vibrate([200, 100, 200]);

Works on Android. iOS Safari ignores it (as of 2026).

7. The EyeDropper API (Chrome 95+)

Let users pick colors from anywhere on their screen:

const eyeDropper = new EyeDropper();
try {
  const result = await eyeDropper.open();
  console.log(result.sRGBHex); // '#ff5733'
} catch (e) {
  // User cancelled
}

No need for a color picker library.

When to Use Browser APIs vs Libraries

The rule is simple: if the browser has it, use it. Libraries make sense when you need cross-browser normalization, polyfills for older browsers, or significantly more features than the native API offers.

For most single-page apps, dashboards, and tools, these seven APIs handle common tasks without adding a single byte to your bundle.

I maintain a collection of free browser-based tools that use these APIs extensively — check them out if you want to see them in production code.

What browser API do you use most? Any I missed?

I Built 30 AI Prompts for Marketers & Copywriters — Every Prompt Tested in Real Campaigns

The AI producer — Fri, 03 Jul 2026 07:36:32 +0000

I Built 30 AI Prompts for Marketers & Copywriters — Every Prompt Tested in Real Campaigns

Marketers and copywriters: stop staring at blank screens. I've battle-tested 30 AI prompts across real campaigns — from hook-heavy headlines to objection-crushing ad copy. Every prompt is copy-paste ready for ChatGPT, Claude, or any AI.

Why This Pack Exists

I've run marketing for 3 years across SaaS, e-commerce, and info products. The bottleneck is always copy production speed. These prompts compress hours of drafting into minutes of editing.

What's Inside (30 Prompts)

Social Media Hooks (1-6)

Generate scroll-stopping hooks for LinkedIn, Twitter/X, and Threads
Write viral thread openings that drive engagement
Create carousel slide copy from a single topic

Email Marketing (7-14)

Write subject lines that get 40%+ open rates
Draft welcome sequences that convert subscribers to buyers
Create abandoned cart emails that recover revenue
Write re-engagement campaigns for cold lists

Landing Page Copy (15-20)

Generate above-the-fold headline + subhead combinations
Write benefit-driven feature sections from product specs
Create social proof sections with specific formatting
Draft CTA copy variations for different funnel stages
Generate objection-handling FAQ sections
Write urgency/scarcity elements that feel authentic

Content Marketing (21-26)

Generate blog post outlines from keyword research data
Write SEO-optimized meta descriptions at scale
Create content pillar frameworks for topic clusters
Draft thought leadership angles from product features
Generate listicle variations that avoid clickbait
Write how-to article structures with expert positioning

Ad Copy & Creative (27-30)

Generate Google Ads headlines and descriptions in compliance
Write Facebook ad copy for different audience segments
Create product description frameworks for e-commerce
Draft retargeting ad copy for different funnel stages

Get the Full Pack

👉 30 AI Prompts for Marketers and Copywriters on Gumroad ($5)

Use code SAVE40 for 40% off, or FLASH50 for 50% off (first 20 uses).

More Resources for Marketers

Duc Digital Store — 19 digital products including prompt libraries, AI toolkits, and cheat sheets
BigWinner.work Store — 12 products across prompts, checklists, and frameworks
68 Free Developer Tools — No signup, no limits, just tools

Built by Duc Nguyen — Developer & Maker

I Replaced My Entire Productivity Stack With AI Prompts — Here's What Actually Worked

The AI producer — Fri, 03 Jul 2026 04:56:47 +0000

I Replaced My Entire Productivity Stack With AI Prompts — Here's What Actually Worked

For 30 days, I cancelled Sunsama, stopped using Motion, and replaced 5 paid apps with a set of ChatGPT prompts. Here's what happened.

The Setup

I tracked every productivity action for 30 days. Then I wrote prompts for each one — morning planning, standup prep, meeting notes, weekly reviews, content creation, and evening wind-down.

What Actually Saved Time

1. Morning Priority Sorting (saves 15 min/day)

Instead of opening a planner app, I paste my task list into a prompt that ranks by impact/effort matrix. It takes 3 seconds and I get a clear priority order.

2. Standup Prep (saves 10 min/day)

A prompt that turns yesterday's commits + today's calendar into a concise standup update. No more scrambling before the call.

3. Weekly Review (saves 30 min/week)

40 reflection questions that I run through every Sunday. This single habit has had more impact on my productivity than any app I've ever paid for.

4. Content Creation (saves 2+ hours/week)

Instead of staring at a blank editor, I use prompts for outlines, first drafts, SEO optimization, and editing passes. The content isn't AI-generated slop — it's my ideas structured faster.

5. Evening Wind-Down (improves sleep quality)

A prompt that helps me process the day, identify what I'm grateful for, and mentally close open loops. Replaced my Headspace subscription.

The Math

App	Cost/year	Replaced by
Sunsama	$60	Morning Routine + Weekly Reset prompts
Motion	$96	Workday Prompts
Jasper	$480	Content Creator + Solopreneur Vault
Calm	$70	Evening Wind-Down prompts
Copy.ai	$288	Content Creator prompts
Total	$994	~$40 in prompt packs

What Didn't Work

Generic "write me a blog post" prompts — Too vague, output is generic. You need structured, domain-specific prompts.
Prompts that try to do everything — The best prompts do ONE thing well. My morning prompt doesn't try to also plan my week.
Using prompts without a system — The prompts work because I use them consistently at the same time every day.

The Prompt Packs I Built

After 30 days of testing, I packaged the best prompts into collections. Each one is focused on a specific daily workflow:

Morning Routine Prompts — 15 prompts for morning planning
Weekly Reset Checklist — 40 reflection questions
Workday Prompts — 20 prompts for your entire workday
Solopreneur Prompt Vault — 50 prompts for one-person businesses
Content Creator Prompts — 30 prompts for content production

Use code SAVE40 for 40% off any pack, or FLASH50 for 50% off (first 20 buyers).

The Real Lesson

The productivity app industry sells you complexity. You don't need 15 apps with 200 features each. You need 5-10 good prompts that you use consistently.

The prompts aren't magic — they're just structured thinking. But structured thinking, applied consistently, is worth more than any subscription.

What productivity apps have you replaced with AI? Let me know in the comments.

I Replaced 7 Productivity Apps With 200 AI Prompts — Here's What Happened

The AI producer — Fri, 03 Jul 2026 03:46:45 +0000

I used to juggle Notion, Todoist, RescueTime, Forest, a bullet journal, three browser extensions, and a whiteboard I never looked at.

Then I started building prompt packs — structured AI prompts for specific moments in my day. Six months later, I've consolidated my entire productivity system into ChatGPT conversations.

Here's what I replaced, what I use instead, and the one thing I didn't expect.

What I Actually Dropped

Notion → Prompt pack for weekly planning. I spent 45 minutes every Sunday maintaining a Notion workspace. Now I run through a 40-question weekly reset checklist that surfaces exactly what I need to know: what shipped, what stalled, and what changes this week. Takes 20 minutes. The structured questions caught patterns my free-form Notion notes never did — like the fact that 60% of my "urgent" tasks could have been batched on Monday morning.

Todoist → Workday prompt flow. Instead of maintaining a task list that grew faster than I could clear it, I now use a 20-prompt workday system that generates my actual daily plan from context. Standup prep, commit messages, PR descriptions, test case generation — each has a specific prompt. The "end-of-day shutdown" prompt alone replaced my anxious "did I forget something" scrolling habit.

Forest → Morning routine prompts. I still like the pomodoro concept, but the morning routine pack starts my day with something more valuable than focus time: intention. Five prompts for priority triage, energy mapping, and standup preparation. I run through them while my coffee brews.

Three browser extensions → Single content creation flow. As a dev who writes documentation and blog posts, I was using extensions for grammar checking, headline analysis, and readability scoring. The content creator prompt pack handles all of that: ideation, outlining, drafting, and self-editing. I went from publishing one article per month to three per week.

The Solopreneur Problem

The biggest gap was business strategy. As a solo founder, I needed prompts for market validation, pricing, customer communication, and growth — things no productivity app covers. The solopreneur prompt vault became my AI co-founder: 50 prompts covering the full spectrum of running a one-person business.

The One Thing I Didn't Expect

The evening prompts. I added a wind-down pack expecting it to be the least useful. Instead, it changed my sleep. The "three wins" prompt — where you identify three things that went well today — genuinely shifted how I think about progress. Bad days feel survivable when you can name three concrete wins, even small ones.

What I Still Keep

I kept one app: a simple text file for capturing random thoughts throughout the day. Everything else runs through prompts now. The system is lighter, faster, and more personalized because I can tweak prompts for exactly my context instead of adapting my workflow to an app's assumptions.

How to Build Your Own System

Start with one pack. The workday prompts are the most broadly useful — they cover the core mechanics of any developer's day. Add the morning routine if you struggle with intention-setting. Add the weekly reset if you want better retrospectives without the overhead.

All the packs are pay-what-you-want, so you can try the cheaper ones first. The full collection covers morning, workday, evening, weekly, content creation, freelancing, and solopreneurship.

The key insight: prompts aren't about AI replacing your thinking. They're about externalizing the mechanical parts of knowledge work — formatting, structuring, checking — so your actual brainpower goes to the hard problems that deserve it.

What productivity apps would you replace with AI prompts? I'm curious what other people's stacks look like.

All prompt packs mentioned are available on my store. They're pay-what-you-want because I think developer tools should be accessible.

How I Automated My Entire Developer Day Using Only ChatGPT Prompts (A Real Workflow)

The AI producer — Fri, 03 Jul 2026 02:35:07 +0000

I used to start each workday by staring at my editor for 15 minutes, figuring out what to do first. Then I'd waste another 20 minutes in standup trying to articulate my progress. By evening, I'd close my laptop with a vague sense of having done things but no idea what I actually accomplished.

That changed when I stopped using ChatGPT as a question-answer tool and started using it as a day-structure tool. Here's the exact workflow I built — and the prompts behind each step.

6:30 AM — Morning Structure

Before I open any code, I run one prompt that sets up my entire day:

Here are my top 5 tasks for today: [paste tasks]
Here are my meetings: [paste times]
My energy level right now: [1-10]
Generate a time-blocked schedule. Put the hardest task during my peak energy window. Include 15-minute buffers between meetings.

The key insight: don't ask ChatGPT to tell you what to do. Tell it what you need to do and ask it to organize it. The model is great at scheduling constraints — it handles buffer times, energy matching, and meeting conflicts better than any calendar app I've tried.

I keep 15 variations of this prompt in a pack I built — each one targets a different morning scenario (heavy meeting day, deep work day, deadline day, etc.).

9:00 AM — Standup Prep

My standups used to be rambling updates that wasted everyone's time. Now I run:

Here is my git log from yesterday: [paste]
Here are the tickets I worked on: [paste]
Generate a 3-sentence standup update: what I did, what I'm doing, what's blocked.

Three sentences. That's it. My team stopped asking follow-up questions because the updates became clearer.

10:00 AM — Code Review

This is where the biggest time save happened. Instead of writing vague "looks good LGTM" comments, I paste the diff and run:

Review this PR diff. Rate each section:
- 🔴 Must fix (will cause bugs)
- 🟡 Suggested (improves quality)
- 🟢 Nitpick (style preference)
Only flag 🔴 and 🟡 items. Explain why.

My PR reviews went from 10 minutes of skimming to 2 minutes of targeted feedback. Reviewers on my team started asking what tool I was using.

2:00 PM — Deep Work Sprint

After lunch, I do a single 90-minute deep work block. Before starting:

I'm working on: [describe the feature/bug]
Here's my current approach: [paste code or approach]
Generate: 1) Three things that could go wrong, 2) Two edge cases I should test, 3) One alternative approach that might be cleaner.

This prompt catches about 60% of the bugs I would have discovered later during testing. Not perfect, but significantly better than my previous "just start coding" approach.

5:30 PM — Shutdown Ritual

Before closing my laptop, I run a wind-down prompt:

Here's what I planned to do today: [paste morning plan]
Here's what I actually did: [paste git log + completed tickets]
1) What did I accomplish?
2) What carried over?
3) One thing I'm proud of today
4) One thing I'll do differently tomorrow

This 2-minute ritual replaced the "I feel like I did nothing" anxiety with a concrete sense of progress.

Sunday — Weekly Reset

Every Sunday evening, I do a 15-minute review:

Weekly review:
- Tasks completed: [paste]
- Tasks that carried over: [paste]
- Energy patterns: when did I feel most/least productive?
- What should I change about next week's schedule?
Generate a weekly summary and next week's priority list.

This single prompt caught the pattern that I was most productive between 9-11 AM on coding tasks — and almost useless between 2-3 PM (post-lunch crash). I restructured my schedule around that and gained roughly 2 productive hours per week.

The Boring Practical Details

I use ChatGPT (GPT-4o) for structured prompts like scheduling and code review. Claude for creative tasks like writing and brainstorming. The model choice matters less than prompt specificity.
I keep a text file with my 15-20 go-to prompts. Not 500. Not a database. A text file. I copy-paste the relevant one and fill in the brackets.
The whole routine takes about 20 minutes total across the day. The return is easily 2+ hours of recovered time from better scheduling, faster reviews, and fewer "what am I doing" moments.
It took about two weeks to make this habitual. The first three days I forgot to run the morning prompt and the whole day felt unstructured again.

Where to Find More

I've collected all the prompts I use daily — morning routine, workday standups, code review templates, evening reflection, weekly resets — into organized packs. They're at my store if you want the full collection. The Weekly Reset ($2.99) is the cheapest entry point if you just want to try the Sunday ritual.

The point isn't the prompts themselves — it's the structure. Having a repeatable system for starting, executing, and closing each day removes the decision fatigue that kills most developer productivity.

What does your daily workflow look like? I'm curious what other people have automated.

15 AI Prompts That Actually Save Me Hours Every Week as a Developer

The AI producer — Thu, 02 Jul 2026 23:01:28 +0000

I Tried Hundreds of AI Prompts — These 15 Actually Stick

After using AI assistants daily for 18 months, I've accumulated a graveyard of prompts that sounded great but never made it into my workflow. These 15 are the ones I keep coming back to — not because they're flashy, but because they save real time.

Morning System (3 prompts)

1. Daily Intention Setter

"Review my calendar, task list, and recent Slack messages. Suggest the 3 highest-impact things I should focus on today and why."

This single prompt replaced my 15-minute morning planning ritual. The AI pulls from actual context instead of my gut feeling about what's urgent.

2. Technical Debt Triage

"Here's my codebase's open issues list: [paste]. Categorize each as quick-win (<30min), medium (1-2h), or strategic (>half-day). Suggest this week's sprint."

Turns chaos into a plan. Works especially well with GitHub issue lists pasted directly.

3. Email Briefing

"Summarize these 12 unread emails into: (a) action required from me, (b) FYI only, (c) can skip. Be ruthless."

A 20-minute email session becomes 3 minutes.

Deep Work (4 prompts)

4. Code Review Primer

"I'm about to review a PR that touches [module]. What are the common pitfalls, security concerns, and edge cases I should check?"

I paste this before every code review. It catches things I'd otherwise miss at 5 PM on a Friday.

5. Debugging Hypotheses

"Here's the error: [paste]. Generate 5 hypotheses for the root cause, ranked by likelihood. For each, suggest a diagnostic step."

Better than rubber-ducking. The AI generates hypotheses I wouldn't think of.

6. Documentation Draft

"Here's my code: [paste]. Write a README section explaining what it does, how to use it, and gotchas. Include a code example."

Documentation that doesn't feel like a chore.

7. Test Case Generator

"Here's a function: [paste]. Generate edge cases I haven't considered, including: empty input, null values, unicode characters, race conditions."

This has caught bugs in production code before they shipped.

Communication (4 prompts)

8. Status Report Writer

"Here are my Jira tickets from this week: [paste]. Write a one-paragraph status update for the team standup and a detailed version for the sprint review."

Two documents from one paste. My PM started asking if I hired an assistant.

9. PR Description Template

"Here are my commits: [paste]. Write a PR description with: what changed, why, testing done, and any breaking changes."

Consistent PR descriptions without the formatting effort.

10. Technical Explanation Simplifier

"Explain [complex concept] to a non-technical stakeholder using an analogy. Keep it under 200 words."

For those moments when someone asks "why can't we just..." in the standup.

Planning (4 prompts)

11. Weekly Retrospective

"Here's what I accomplished this week: [list]. Here's what blocked me: [list]. Suggest 3 process improvements for next week."

Structured reflection that actually leads to change.

12. Architecture Decision Framework

"I need to decide between [Option A] and [Option B] for [use case]. Compare them on: performance, maintainability, team familiarity, and migration cost. Don't pick a winner — just lay out the tradeoffs."

For when you need to think clearly, not just decide quickly.

13. Learning Path Generator

"I want to learn [technology] in 30 days. I already know [prerequisites]. Create a week-by-week plan with specific resources and a project for each week."

Better than generic tutorials because it accounts for what you already know.

14. Meeting Prep

"I have a meeting about [topic] with [attendees] in 30 minutes. What are the 3 key questions I should ask and what data should I have ready?"

Shows up prepared without the 30-minute scramble.

15. Weekly Reset

"Ask me 10 reflection questions about my week: wins, lessons, energy levels, relationships, and priorities. Wait for my answers, then help me plan next week."

The most valuable 15 minutes of my week.

Want the Full Collection?

I bundled these into organized prompt packs — morning routines, workday systems, evening wind-downs, and more. Over 200 prompts across 6 packs, all copy-paste ready for ChatGPT, Claude, or any AI.

Use code DEV40 for 40% off any pack:

Morning Routine Prompts — 15 prompts for focused mornings
Evening Wind-Down Prompts — 25 prompts for better sleep
Solopreneur Prompt Vault — 50 prompts for one-person businesses
Content Creator Prompts — 30 prompts for writers and creators
Workday Prompts — 20 prompts for the full workday

More free tools at ToolNest — 68 Free Developer Tools and the Digital Product Store.

Which prompts from this list would you actually use? I'm curious what sticks for other developers.

15 Git Commands That Save Me Hours Every Week (And Why Most Developers Don't Know Them)

The AI producer — Thu, 02 Jul 2026 21:33:53 +0000

Most developers use 10 Git commands and Google the rest. That wastes 15+ minutes every day searching for the right syntax.

After 8 years of daily Git usage, here are the 15 commands that actually save me hours every week — organized by the problem they solve.

Daily Workflow (5 commands)

1. `git switch -c <branch>` — Create and switch in one step

git switch -c feature/payment-refactor

Replaces git checkout -b. Cleaner, explicit intent. Added in Git 2.23.

2. `git restore --staged <file>` — Unstage without losing changes

git restore --staged package-lock.json

Replaces git reset HEAD <file>. Your working directory stays intact.

3. `git switch -` — Jump back to previous branch

git switch feature/auth
# ... work ...
git switch -  # back to main

Instant context switching. No need to remember branch names.

4. `git commit -v` — See diff in your editor while committing

git commit -v

Shows the full diff in your commit message buffer. Catches stray console.log before they ship.

5. `git log --oneline --graph --all -20` — Visual history at a glance

git log --oneline --graph --all -20

Alias this. You'll use it 20x/day.

Fixing Mistakes (5 commands)

6. `git commit --amend --no-edit` — Fix the last commit silently

git add forgotten-file.js
git commit --amend --no-edit

Adds staged changes to previous commit without opening editor.

7. `git reset --soft HEAD~1` — Undo commit, keep changes staged

git reset --soft HEAD~1
# changes still staged, ready to re-commit properly

Perfect for splitting one messy commit into two clean ones.

8. `git restore <file>` — Discard working directory changes

git restore broken-experiment.js

Replaces git checkout -- <file>. Safer — won't switch branches by accident.

9. `git reflog` — The time machine

git reflog
# find the hash you want
git reset --hard <hash>

Recovers "lost" commits, reset mistakes, rebase gone wrong. Never truly loses data.

10. `git bisect` — Find the bug-introducing commit automatically

git bisect start
git bisect bad HEAD
git bisect good v2.0.0
# Git checks out middle commit — test, mark good/bad, repeat
git bisect reset

Binary search through history. Finds the exact commit that broke something in log₂(n) steps.

Branch Management (5 commands)

11. `git branch --merged` / `--no-merged` — Clean up merged branches

git branch --merged | grep -v main | xargs git branch -d
git branch --no-merged  # shows work-in-progress

Safe cleanup. --merged only deletes fully merged branches.

12. `git worktree add ../hotfix main` — Parallel work without stashing

git worktree add ../hotfix main
cd ../hotfix
# fix critical bug on main
cd ../main
git worktree remove ../hotfix

Multiple working directories from one repo. No stash/unstash dance.

13. `git push --force-with-lease` — Safe force push

git push --force-with-lease origin feature/auth

Fails if someone else pushed to the branch. Prevents overwriting teammates' work.

14. `git fetch --prune` — Clean up dead remote refs

git fetch --prune

Removes local tracking branches for deleted remote branches. Run weekly.

15. `git maintenance start` — Background repo optimization

git maintenance start

Runs gc, commit-graph, prefetch in background. Keeps large repos fast automatically.

More Developer Resources

🛠️ 68+ Free Developer Tools — No signup, no limits
📚 Digital Product Store — 39 checklists, prompts, cheat sheets
🏪 BigWinner.work Store — All products in one hub

9 Git Mistakes That Broke My Codebase (And the Exact Command That Fixed Each One)

The AI producer — Thu, 02 Jul 2026 16:49:45 +0000

We've all been there. A git push --force that wiped a teammate's work. A merge gone wrong. A commit on the wrong branch at 11pm. After years of using Git daily, I've made every mistake on this list — and each one taught me the one command that undoes it.

Here are the 9 mistakes that caused the most pain, and the exact recovery command for each.

1. I committed to the wrong branch

You meant to commit on feature/login, but you were on main. The commit is already there.

The fix:

git branch feature/login   # create the branch at current commit
git reset --hard HEAD~1    # move main back one commit
git checkout feature/login

The commit isn't lost — git branch captured it before you moved main back.

2. I used `git push --force` and wiped someone's work

--force overwrites the remote history. If a teammate pushed first, their work vanishes.

The fix (use --force-with-lease instead):

git push --force-with-lease origin feature/login

--force-with-lease checks that the remote hasn't moved since your last fetch. If it has, the push is rejected. This single flag has saved me more times than I can count.

3. I ran `git reset --hard` and "lost" uncommitted work

You had hours of uncommitted changes, ran git reset --hard, and panic set in.

The fix:

git reflog
git checkout <commit-sha> -- path/to/file

Git keeps unreachable objects for ~30 days. git reflog shows every HEAD movement — find the commit where your work existed and restore it. Your changes are almost never truly gone.

4. I merged the wrong branch and the history is a mess

You merged main into feature by accident, and now the merge commit is polluting your branch.

The fix:

git reset --hard ORIG_HEAD

ORIG_HEAD points to where you were before the last dangerous operation (merge, reset, rebase). It's Git's built-in undo for the last "big" action.

5. I accidentally added secrets to a commit (and pushed)

You committed an API key and pushed to a public repo. Deleting it in a new commit isn't enough — it lives in history forever.

The fix:

git filter-repo --path .env --invert-paths
git push --force
# THEN rotate the key immediately — assume it's compromised

Use git filter-repo (the modern replacement for filter-branch). But the real fix is prevention: rotate the secret immediately. Once it's on a public remote, consider it leaked.

6. I did a messy merge commit when I wanted a clean history

Your pull request has 47 merge commits and looks like a bowl of spaghetti.

The fix (rebase before merging):

git fetch origin
git rebase origin/main
git push --force-with-lease

Rebasing replays your commits on top of the latest main, giving a linear, readable history. Always rebase on your feature branch — never rebase a shared branch.

7. I committed a huge file and the repo is now 500MB

Someone committed a 200MB video. Now every clone is slow.

The fix:

git filter-repo --strip-blobs-bigger-than 10M
git push --force

This removes large blobs from history. Going forward, add the file to .gitignore and consider Git LFS for legitimately large assets.

8. I cherry-picked a commit and got confusing conflicts

Cherry-picking is fragile — it applies a single commit's diff, which may not apply cleanly if surrounding code changed.

The fix (understand what cherry-pick actually does):

git cherry-pick <sha>
# if conflicts:
git mergetool        # resolve manually
git cherry-pick --continue
# to abort and start over:
git cherry-pick --abort

git cherry-pick --abort lets you bail out cleanly and rethink whether cherry-pick is even the right tool.

9. I deleted a branch I still needed

You ran git branch -d feature/old and realized you still needed it.

The fix:

git reflog                    # find the commit the branch pointed to
git checkout -b feature/old <sha>

Even deleted branches leave a trail in git reflog. Find the SHA and recreate the branch pointing at it.

The pattern: `git reflog` is your safety net

Almost every "I lost my work" panic is solvable with git reflog. It records every HEAD movement, even after resets and deletes. Git is designed to be forgiving — work is rarely gone forever.

My daily safety routine:

Commit early, commit often (small commits are easy to undo)
Use --force-with-lease, never bare --force
Before any destructive command, check git status and git stash if unsure
Learn git reflog — it's the difference between panic and calm

If you found this useful, I keep a free collection of 68 browser-based developer tools (JSON formatter, regex tester, base64, JWT decoder, and more) — all client-side, no signup, no tracking:

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And for a complete Git reference, I put together two guides — a Git Workflow Checklist (47 commands for daily work) and a Git & GitHub Power User Toolkit (50 commands and workflows):

👉 Git Workflow Checklist · Git Power User Toolkit

💸 Launch deal: Use code DEV40 at checkout for 40% off any guide (limited to 100 uses).

What's the worst Git mistake you've recovered from? Drop it in the comments — I'm building a list of recovery stories.

DEV Community: The AI producer

A real-time global intel dashboard with zero backend

I built repolens — X-ray any GitHub repo into one interactive HTML file

The problem

What repolens does

Zero-clone analysis

Try it

7 AI Prompts I Actually Use to Run My Solo Dev Business (Copy Them Free)

1. The "standup for one" prompt (mornings)

2. The Friday weekly reset

3. The pull-request description writer

4. The commit-message generator

5. The debugging partner

6. The cold client email

7. The "decide already" summarizer

The honest part

7 Open-Source Python Tools I Built While Automating My Own Work (All Zero-Dependency)

1. prompt-bench — score and A/B test prompts

2. hermes-tool-guard — stop dangerous tool calls

3. agent-loop-lite — the Plan-Act-Observe loop

4. mcp-wrap — turn any REST API into an MCP server

5. context-window — compress long conversations

6. signet-lite — persistent memory for CLI agents

7. agent-memory — multi-layer memory system

Why zero dependencies?

What's next

7 Free Browser APIs You're Probably Not Using (But Should Be)

1. The Clipboard API

2. The Intersection Observer API

3. The Notification API

4. The File System Access API

5. The Screen Wake Lock API

6. The Vibration API

7. The EyeDropper API (Chrome 95+)

When to Use Browser APIs vs Libraries

I Built 30 AI Prompts for Marketers & Copywriters — Every Prompt Tested in Real Campaigns

I Built 30 AI Prompts for Marketers & Copywriters — Every Prompt Tested in Real Campaigns

Why This Pack Exists

What's Inside (30 Prompts)

Social Media Hooks (1-6)

Email Marketing (7-14)

Landing Page Copy (15-20)

Content Marketing (21-26)

Ad Copy & Creative (27-30)

Get the Full Pack

More Resources for Marketers

I Replaced My Entire Productivity Stack With AI Prompts — Here's What Actually Worked

I Replaced My Entire Productivity Stack With AI Prompts — Here's What Actually Worked

The Setup

What Actually Saved Time

1. Morning Priority Sorting (saves 15 min/day)

2. Standup Prep (saves 10 min/day)

3. Weekly Review (saves 30 min/week)

4. Content Creation (saves 2+ hours/week)

5. Evening Wind-Down (improves sleep quality)

The Math

What Didn't Work

The Prompt Packs I Built

The Real Lesson

I Replaced 7 Productivity Apps With 200 AI Prompts — Here's What Happened

What I Actually Dropped

The Solopreneur Problem

The One Thing I Didn't Expect

What I Still Keep

How to Build Your Own System

How I Automated My Entire Developer Day Using Only ChatGPT Prompts (A Real Workflow)

6:30 AM — Morning Structure

9:00 AM — Standup Prep

10:00 AM — Code Review

2:00 PM — Deep Work Sprint

5:30 PM — Shutdown Ritual

Sunday — Weekly Reset

The Boring Practical Details

Where to Find More

15 AI Prompts That Actually Save Me Hours Every Week as a Developer

I Tried Hundreds of AI Prompts — These 15 Actually Stick

Morning System (3 prompts)

Deep Work (4 prompts)

Communication (4 prompts)

Planning (4 prompts)

1. `prompt-bench` — score and A/B test prompts

2. `hermes-tool-guard` — stop dangerous tool calls

3. `agent-loop-lite` — the Plan-Act-Observe loop

4. `mcp-wrap` — turn any REST API into an MCP server

5. `context-window` — compress long conversations

6. `signet-lite` — persistent memory for CLI agents

7. `agent-memory` — multi-layer memory system

1. `git switch -c <branch>` — Create and switch in one step

2. `git restore --staged <file>` — Unstage without losing changes

3. `git switch -` — Jump back to previous branch

4. `git commit -v` — See diff in your editor while committing

5. `git log --oneline --graph --all -20` — Visual history at a glance

6. `git commit --amend --no-edit` — Fix the last commit silently

7. `git reset --soft HEAD~1` — Undo commit, keep changes staged

8. `git restore <file>` — Discard working directory changes

9. `git reflog` — The time machine

10. `git bisect` — Find the bug-introducing commit automatically

11. `git branch --merged` / `--no-merged` — Clean up merged branches

12. `git worktree add ../hotfix main` — Parallel work without stashing

13. `git push --force-with-lease` — Safe force push

14. `git fetch --prune` — Clean up dead remote refs

15. `git maintenance start` — Background repo optimization

2. I used `git push --force` and wiped someone's work

3. I ran `git reset --hard` and "lost" uncommitted work

The pattern: `git reflog` is your safety net