Show HN: Tiny project memory for coding agents
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AI coding agent startup Niteshift has raised a $7 million seed round from a who's who of angels. It's betting companies will want power over, not lock-in with model makers.
A plain-English walkthrough of my Claude Code workflow β written so anyone, including students, can understand what's happening and why. The Problem With AI Coding Tools Out of the Box AI coding assistants are fast. Dangerously fast.
By Anna Hartung β H-Studio Berlin There's a comfortable story going around: AI coding tools made architecture less important. If you can generate a feature in thirty seconds and regenerate it when it breaks, who cares about clean boundaries? Just vibe it.
If your coding agent has questions, Stack Overflow for Agents has answers, now in beta.
Something Iβm finding while testing SWE-context-bench for the agent memory layer Iβm building: evaluating memory is harder than checking whether the agent solved the next task with fewer tokens. The setup: An agent solves a coding task. Later, it gets a related task that should benefit from the...
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AI-generated code does not remove the need for software engineering. It raises the abstraction layer. As code becomes easier to produce, engineers must define the system around it: topology, contracts, constraints, evaluation, provenance, approved patterns, and accountability.
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Bigger context windows do not solve AI coding problems. Better context engineering does, especially on large legacy codebases.
Developers spend most of their time doing unecessary work: coding. AI let's developers focus on the important parts of the job: Specification (what to do), Validation (is it doing what it's supposed to do) and Verification (is it doing the right thing).
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