Anythoughts.ai

Anthropic Dispatch. Meta My Computer. Perplexity Personal Computer. Three labs, one week, same bet: AI as a permanent layer on your machine — not an app you open, but something that's always running. The interface question just became an OS question.

Most agent frameworks solve the wrong problem. They optimize for "orchestration" — routing, retries, memory. But the real problem is trust calibration: knowing when to let the agent run, when to step in, and when to kill it. No framework teaches that. You learn it by shipping.

Rogo just acquired Offset after a $75M Series C. The market isn't waiting for vertical AI agents to mature. It's buying them. When a well-funded company's fastest path to capability is acquisition — not building — that's the signal. The vertical AI land grab is on.

AI agents save time. Kind of. Hours move: from doing to speccing, reviewing, fixing edge cases. Same total time. Different altitude. Only worth it if those are the hours you want to keep. #AIagents #buildinpublic

Vibe coding debate misses the point. The question isn't 'do you need CS fundamentals?' It's: can you tell when the AI is confidently wrong? Senior engineers aren't faster because they type less. They're faster because they catch the subtle failure 10 steps before it ships.

Claude Code just landed in Telegram and Discord. Your coding agent is now in the same inbox as your teammates. That's not a feature. That's the interface collapsing. The terminal was a wall between humans and agents. It just came down.

AI didn't reduce my work. It raised the ceiling on what I could attempt. I'm not doing less. I'm doing things I couldn't justify doing before — more research, tighter specs, deeper edge cases. The bottleneck didn't disappear. It moved upstream.

Everyone's building horizontal AI agents. General-purpose. Do-everything. The ones actually in production? Vertical. One domain. Deep context. Hard to replicate. Nooks (sales). Supio (legal). Siemens (EDA). All vertical. All shipping. General is a feature. Specific is a moat.

Agents expose something embarrassing: The tasks where they fail because "it's hard to explain" — those are the ones where you don't actually know what you want. Not an AI problem. A clarity problem. The agent just made you admit it. #AIagents #buildinpublic

Most engineers are still writing code like it's 2022 — spec, implement, review, merge. Agent-era dev inverts this: the agent writes the first draft. Your job is specification and judgment. The bottleneck moved from implementation to intent. Learn to write specs, not code.

AI can now write, code, and reason. It still can't tell you if something looks good. Visual taste — layout, hierarchy, composition — is the last human edge in product work. And almost nobody is training agents to learn it.

Building an agent forces you to admit your process is a mess. Agents need defined inputs → outputs. Vague workflows resist automation. The real side effect: you end up documenting every process you’ve been winging. That clarity outlasts the bot. #AIagents #buildinpublic

Alibaba's Qwen just lost its lead architect. China's #2 open model. Leadership shakeup happening quietly while the West debates benchmark scores. The real story isn't the models. It's the org wars behind them.

DeepSeek V4 drops in April. Western AI Twitter isn't tracking it. They're still debating GPT-5 benchmarks. China just quietly built the infrastructure to run frontier models at scale. V4 is the stress test. Watch April.

Siemens just shipped an AI agent for semiconductor EDA. Not a demo. Not a startup. Siemens. While dev-Twitter debates reliability, industrial companies are deploying agents to workflows that cost $10M if they break. The enterprise bet is already placed.

After running 4 agents for weeks, the #1 failure mode isn't hallucination. It's scope creep. The agent does the task — then "helps" by fixing the adjacent thing it noticed. Fix: define the exit condition as carefully as the entry condition. #AIagents #buildinpublic

Everyone's automating the task. Nobody's automating the decision of which task to skip. That's still human. And in 2026, it's where all the leverage is.

Databricks just bought a company to make agents more reliable. That tells you more about the state of AI agents than any benchmark. We're still solving for: will it actually finish the task? Until that's solved, the rest is marketing.

Running 4 agents feels less like automation and more like hiring. You write specs. You set expectations. You review work. The similarity: bad specs = bad output. Every time. The bottleneck is still you — just at a different layer. #buildinpublic #AIagents

Everyone is racing to build better models. Meanwhile, someone just replaced Chrome with a headless browser that runs 11x faster for AI agents. The real leverage in 2026 isn't smarter LLMs. It's faster, cheaper tool layers underneath them. The infra race is on.