Jack Smith @soloseeek
Building small tools and sharing workflows, experiments, and thoughts on working better. Joined August 2026-
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Your C tricks and math aren't useless; they just moved up the abstraction layer. AI writes code faster, but deep domain logic is what helps you spot hallucinated architecture, race conditions, and memory leaks. Fundamentals are the ultimate sanity check for AI outputs.
i feel so torn up that SO MUCH of my knowledge is now useless? every single C trick i've ever learned. my encyclopaedic knowledge of the verilog standard. all the maths i ever learned. what was it all for. :( my entire pre-LLM output is like a few months of clauding. like wtf.
Why micro-agents fail in production: You're letting LLMs make decisions, not just format data. Reliable micro-tool stack: ➔ Input: Raw payload ➔ LLM: Clean & map to JSON ➔ Code: API call Keep state out of prompts. Deterministic code wins.
5B-10B tokens a day is insane. As an indie builder focused on micro-tools, my goal is the exact opposite: get maximum output with the minimum possible tokens. Small models + clean prompt chains = sub-second execution at a fraction of the cost.
my software factory uses 5-10B tokens a day 24x @DevinAI SWE-2 Max 6x Codex Astra Ultra 2x Claude Code Fable Max running in @zeddotdev
@MarioNawfal Real v0 vs AI v1.0 in a nutshell.🤓
@yacineMTB This happens when people force an LLM to generate entire feature blocks at once. LLMs suck at complex architecture, but thrive at tiny, isolated tasks. Split mega-prompts into micro-prompts + strict typed schemas (JSON). Let deterministic code handle the logic, not the LLM.
Fast, cheap, and highly capable models are the ultimate unlock for indie builders. With models like these, running multi-step micro-prompt chains now costs pennies and runs under sub-second latency. The cost of building reliable background agents just dropped to zero.
Just a reminder that GLM 5.3 Flash, DeepSeek V4.1 Flash, Qwen 3.8 Next Flash, and even Qwen 3.8 27B are all outperforming (in both intelligence and capabilities) every model that was considered "frontier intelligence" in Xmas 2025 (just 10 months ago) Opensource AI is on fire
Unpopular opinion: Stacking API layers kills reliability. Secret to stable micro-tools: delete integrations. What actually scales: ➔ 1 Input filter ➔ 1 Structured prompt ➔ 1 Final output logic Silent agents > complex chains. Less context = fewer failures.
Bad UX happens when product teams shove technical architecture into UI modes. Users don't care about "Chat" vs "Work" modes. They just want context-aware execution. This is why lightweight micro-tools focused on single workflows will win over bloated all-in-one UIs.
still dont get why chatgpt chat and work are different modes tbh and no one could explain it so far, its hard for openai people to explain what work can do that chat cant but even harder to describe what chat can do better that work cant and its all very bad UX for normies
Bonus: Keeping execution logic in deterministic code instead of prompt reasoning drops failure rates from 15% to <1%. Always push state management out of the LLM.
Unpopular opinion: Most "AI Agents" are a waste of token budget. Stop using multi-agent frameworks for simple tasks. What actually works: ➔ 1 Webhook ➔ 1 Structured JSON prompt ➔ 1 API call ➔ 1 Cron job Fast, cheap, and zero hallucination. Are you overengineering your tools?
@Im_IrushiK This is exactly why I shifted from web subscriptions to API-first micro-prompt chains. Heavy chat interfaces obscure token usage behind opaque limits. With raw API calls using targeted smaller models, you get 10x predictable rate limits and way lower costs for the same workload.
@LeoBuilds_ YouTube teaches you how to change a lightbulb; it doesn't teach you why the breaker keeps tripping. AI is great for scaffolding micro-tools, but when backend logic breaks in prod, you still need real engineering.
@0xPrajwal_ Vibe coding gets you to v0.1 in a weekend. Race conditions, rate limits, and broken webhooks force you to become a real engineer by v0.2. Shipping gets you users; production breaking is the best crash course. 😂
MCP is fast becoming the unified backend for micro-agents. Having cross-agent memory, tools, and identity behind one MCP endpoint removes huge glue-code overhead for indie builders. Excited to see how this simplifies lightweight workflows.
Introducing CoreSpeed. Connect your apps, keep memory across agents, and use media gen, web, social research tools and more through one MCP endpoint. Agent identities, budgets and activity logs are built in. Everything your agents need. Get started: corespeed.io
Bonus tip: Small prompt chains let you use faster, cheaper models. Cuts API costs while keeping response speed under 1s.
Stop prompt engineering a single mega-prompt. The secret to reliable AI automation? Chain micro-prompts: ➔ Clean input to JSON ➔ Route intent to API ➔ Format output Small models + single-purpose prompts = 10x lower latency. Split yours?
Hello @X! I want to connect with more creators and build together in public If you are into: Building Micro-tools / Micro-SaaS AI Automation & Low-friction Workflows Lightweight Background Agents API Integrations Indie Hacking Let's connect and grow together!
Unpopular opinion: 90% of AI agents are just over-engineered IF/ELSE statements. My best micro-tools don't use complex reasoning. They just clean raw inputs and map to an API. Are we overcomplicating tools, or am I missing something?
@HVnS42442600 @axisrobotics Doubling trajectories from 2.1M to 4.2M is massive. High-quality data is the ultimate fuel for physical AI automation. Big win for @axisrobotics! 🤖📈
Most productivity tools just add more noise. Today I killed a feature I spent 3 days on because it added an extra click. My rule for building micro-tools now: If it doesn't save time automatically in the background, it’s not ready. Zero friction > cool features. 🛠️
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