// Writing
sudo make me a blog.
Most private LLM projects stall at simple automations like news summaries or inbox cleanup. But the real goal is a context-aware system that understands your state, integrates fragmented personal data, and actively supports daily decisions. The challenge is no longer model capability—it’s data access, orchestration, and building a unified personal intelligence layer.
The goal feature restored autonomy, but the real takeaway was architectural: use Opus for high-cost reasoning and validation, Sonnet for execution. Attempts to scale with Fable failed early due to token limits, making it impractical for sustained workloads under current constraints.
I assigned Claude Code a large task with a fixed goal and forced it to stay on course. Even with nested Ralph Loops, a queue, and finally an hourly cron job, the agent repeatedly stalled before completing the work. It runs for now, but the experiment exposed a laziness for LLM-based agents.
Agentic coding makes implementation cheap, but for many teams the real bottleneck is now trust: code can be generated faster than it can be confidently reviewed.
If someone asks me…
Secret-Service is a Python MCP server that routes problems through a pipeline of seven specialised LLM-backed agents using a blackboard architecture stored entirely in SQLite. Instead of committing to one solution, it fans out into parallel strategy branches, gates each plan through a Judge before execution, scores the results, and returns the winner. Over time, the system learns: successful strategies are embedded and recalled for future problems; failures become anti-patterns.
DevAgentOps runs LLM agents inside CI/CD with hard gates: analyze role-based use cases/stories, write testable acceptance criteria and verify for contradictions/gaps; design by mapping features to modules, planning, modeling (formal checks where possible), refining to implementation-ready specs, and creating reverse prompts; implement via human-piloted agent coding plus agent review, MR, unit/integration/E2E/role tests, then manual merge - validate behavior and loop back with evidence
The faster we move, the more we need the discipline we abandoned to move fast. Agentic Safety-Critical Development embeds AI agents at every stage — verifying before, validating after, and throwing back errors upstream instantly. This is not slower Agile. This is Agile grown up.
A developer's experiment in giving Claude Code an impossible task, and the surprisingly human way it solved it.
Bloated OS abstraction layers are legacy debt — it's time to replace at least the filesystem with knowledge-driven design.