Atelier faster runtime for coding agents
25% faster while cutting token costs by 30%-67%
Atelier faster runtime for coding agents launched on July 10, 2026 with 19 votes, #902 of 24,623 launches that month and more than 95% of that year's launches. The site was not reachable when checked in September 2026.
What it does
One shot to the right code. Atelier is a context runtime and cost-optimization layer for AI coding agents. Instead of passive indexing or simple output compression, it manages the full context window end-to-end — making it 25% faster while cutting cost by 30% (up to 67% on some workloads), audited head-to-head on SWE-bench. It also ships the strongest code search we've measured: 0.727 MRR vs the best of 10 rival tools' 0.557. Works with Claude Code, Codex, Cursor, and any MCP-compatible agent.
Does a similar job
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BulletAug 2026 · codewithbullet.com · ▲240Hi HN, excited to be sharing this with you guys today. TL;DR: Bullet is a fast coding agent. At our company, we were burning hours waiting on agent runs. The models weren’t the problem. The agent loops around them were slow (unnecessary planning, oversized context, repeated searches, and serial tool calls). So we built Bullet as a passion project, the goal being to help you iterate faster, without giving up quality: - It automatically selects the model and reasoning level for each prompt. - It runs independent searches, reads, and commands in parallel. - It uses targeted code search instead…
Kilo Code for VS CodeMay 2026 · kilo.ai · ▲896Parallel agents, diff reviewer, and multi-model comparisons


Semble – Code search for agents that uses 98% fewer tokens than grepMay 2026 · github.com · ▲445Hey HN! We (Stephan and Thomas) recently open-sourced Semble. We kept running into the same problem while using Claude Code on large codebases: when the agent can't find something directly, it falls back to grep, reading full files or launching subagents. This uses a lot of tokens, and often still misses the relevant code. There are existing tools for this, but they were either too slow to index on demand, needed API keys, or had poor retrieval quality. Semble is our solution for this. It combines static Model2Vec embeddings (using our latest static model: potion-code-16M) with BM25, fused…

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