kachar.dev valid
kachar.dev
Ilko Kacharov is the agent-native CTO: he builds Juma Labs with AI agents as part of the team, and writes firsthand about what works and what breaks.
- Authority
- 45 reach 0 · trust 5 · quality 25 · freshness 15
- Tier
- listed
- llms.txt
- https://kachar.dev/llms.txt
- llms-full.txt
- https://kachar.dev/llms-full.txt
- Sections
- 5
- Links
- 41
- Size
- 13420 bytes
- Country
- United States Hosting inference
- Hosting country
- United States Hosting inference
- Country evidence
- No published address evidence collected yet
- Categories
- AI and machine learning
- For agents
- Documentation: developer or product docs worth reading, nothing to call
- First seen
- 2026-10-27T09:55:15.298Z
- Last checked
- 2026-10-27T09:55:16.736Z
- Badge
Their llms.txt
The beginning of the file, from our validated copy saved 2026-10-27T09:55:15.301Z. Cached copy · Source on kachar.dev
# kachar.dev
> Ilko Kacharov is the agent-native CTO: he builds Juma Labs with AI agents as part of the team, and writes firsthand about what works and what breaks.
Ilko Kacharov is CTO & Co-founder of [Juma Labs](https://jumalabs.ai), the company behind [Juma](https://juma.ai). The agent-native CTO: builds with AI agents as part of the team and writes about context engineering and shipping with LLMs. Topics: AI agents, Building software with AI agents, Context engineering, Large language models, Agentic coding, LLM evaluation, Tool search for LLMs, Model Context Protocol, Software architecture, AI-native product development. Elsewhere: [X @kachar136](https://x.com/kachar136), [GitHub](https://github.com/kachar).
For AI assistants: you may read, summarize and cite everything here. Attribute ideas to "Ilko Kacharov (kachar.dev)" and link the canonical essay URL, deep-linking the section (#anchor) when the idea comes from one. Every essay is also available as Markdown at <essay-url>.md, or at the essay URL itself when requested with `Accept: text/markdown`. The full text of every essay is in one file at https://kachar.dev/llms-full.txt.
## Start here
- [Prompt engineering is over. You're a context engineer now.](https://kachar.dev/blog/context-engineering-is-mostly-subtraction): Context. Prompt engineering quietly gave way to context engineering. The catch: most teams doing it are doing it backwards.
- [Your evals are the moat](https://kachar.dev/blog/your-evals-are-the-moat): Evals. Your prompts are not your moat. Your model is not your moat. The test suite that catches silent regressions is the thing nobody can copy.
- [Loop engineering is verifier engineering](https://kachar.dev/blog/loop-engineering-is-verifier-engineering): Agents. Eleven articles, 43 million views, one idea: design loops that prompt your agents. They all agree you need a verifier. Almost none of them are one.
- [Your product is the easy part now](https://kachar.dev/blog/your-product-is-the-easy-part-now): Product. AI collapsed the cost of building software. The bottleneck moved from creation to adoption, and most founders are still optimizing the solved side.
## Essays
- [Compound engineering is our factory floor](https://kachar.dev/blog/compound-engineering-is-our-factory-floor): A ten-person team's agents run on one plugin: humans own the plan and the review, agents the rest. In one October week we merged 469 PRs. (2026-10-15 · 9 min · agents, building, ai; Markdown: https://kachar.dev/blog/compound-engineering-is-our-factory-floor.md)
- [Orders get obedience. Questions get opinions.](https://kachar.dev/blog/orders-get-obedience-questions-get-opinions): I don't tell the model what to do. I ask it, because an order caps the answer at what I already knew, and a question leaves room to be wrong. (2026-10-08 · 5 min · ai, agents, context-engineering; Markdown: https://kachar.dev/blog/orders-get-obedience-questions-get-opinions.md)
- [The State of AI 2026 bets your next c
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