nowfound

AI · September 9, 2026

Ctrlb-decompose: Strip the noise from logs before sending to LLMs

LLM-ready reasoning surface over logs. Contribute to ctrlb-hq/ctrlb-decompose development by creating an account on GitHub.

Ctrlb-decompose: Strip the noise from logs before sending to LLMs launched on September 9, 2026 with 14 votes, #893 of 8,094 launches that month and more than 90% of that year's launches.

Visit github.com ↗Alternativestop 11% of September 2026

In plain words

Ctrlb-decompose processes large volumes of application logs to extract meaningful patterns and structure, converting noisy data into a format optimized for large language models. It identifies typed variables, quantiles, and anomalies while summarizing millions of log lines into compact, analyzable output. The tool runs as a command-line interface, browser-based WASM application, or Rust library, keeping all log data local to the user's environment.

written from the facts on this page · September 2026

From the sources

Turn millions of noisy log lines into compact patterns with typed variables, quantiles, anomalies, and LLM-ready output. Runs as a CLI, in the browser via WASM, or as a Rust library — no logs ever leave yourfrom github.com

Does a similar job

all alternatives →
  • Langfuse2023 · langfuse.com · ▲771

    Open source tracing and analytics for LLM applications

  • CodeRLM – Tree-sitter-backed code indexing for LLM agentsFeb 2026 · github.com · ▲81

    I've been building a tool that changes how LLM coding agents explore codebases, and I wanted to share it along with some early observations. Typically claude code globs directories, greps for patterns, and reads files with minimal guidance. It works in kind of the same way you'd learn to navigate a city by walking every street. You'll eventually build a mental map, but claude never does - at least not any that persists across different contexts. The Recursive Language Models paper from Zhang, Kraska, and Khattab at MIT CSAIL introduced a cleaner framing. Instead of cramming everything into…

  • Letting LLMs Run a Debugger2025 · github.com · ▲147

    Hey HN, I just built an experimental VSCode extension called LLM Debugger. It’s a proof-of-concept that lets a large language model take charge of debugging. Instead of only looking at the static code, the LLM also gets to see the live runtime state—actual variable values, function calls, branch decisions, and more. The idea is to give it enough context to help diagnose issues faster and even generate synthetic data from running programs. Here’s what it does: * Active Debugging: It integrates with Node.js debug sessions to gather runtime info (like variable states and stack traces). *…

  • I built a tool to un-dumb Claude Code's CLI output (Local Log Viewer)Feb 2026 · github.com · ▲69

    Hi HN, I built this because I got tired of the Claude Code CLI hiding details from me. Recent updates have replaced critical output with summaries like "Read 3 files" or "Edited 2 files". To see what actually happened, I was forced to use `--verbose`, which floods the terminal with unreadable JSON and system prompts. I wanted a middle ground: *Full observability without the noise.* `claude-devtools` is a local Electron app that tails the session logs in `~/.claude/` to reconstruct the execution trace in real-time. *Unlike wrappers, it solves the visibility gap in your native…

  • A new benchmark for testing LLMs for deterministic outputsApr 2026 · interfaze.ai · ▲60

    When building workflows that rely on LLMs, we commonly use structured output for programmatic use cases like converting an invoice into rows or meeting transcripts into tickets or even complex PDFs into database entries. The model may return the schema you want, but with hallucinated values like `invoice_date` being off by 2 months or the transcript array ordered wrongly. The JSON is valid, but the values are not. Structured output today is a big part of using LLMs, especially when building deterministic workflows. Current structured output benchmarks (e.g., JSONSchemaBench) only validate…

  • Open Source Suite for Binary Analysis, Based on QEmu and LLVM2018 · rev.ng · ▲46

    Next-gen decompiler.

More ai this month

the category →
  • Video model that turns script into viral social media videos

    AI · 18d ago · spira.ai

  • The browser where Claude Code and Codex work beside you

    AI · 18d ago · rill.love

  • Kolibri is Aleph Alpha

    AI · 20d ago · tej.as

  • Paintings by AI models: programs run through a simulation of oil paint.

    AI · 21d ago · stillwet.art

  • Beam.win▲378

    Run AI agents on one Mac and use its windows on another

    AI · 5d ago · beam.win

  • Route requests to the right LLM for cost, latency & quality

    AI · 22d ago · fastrouter.ai

Launched alongside, September 2026

the whole month →
  • E-ink bird frame for Raspberry Pi - real-time bird detection by audio, fully local AI, rendered as real, hand-cut 1800s bird illustrations. - arnegiacomo/fugleramme

    AI · Sep 2026 · github.com

  • WI

    Special relativity simulator in which light crawls along at a human speed, so Lorentz contraction, aberration and Doppler shift happen at walking pace.

    Life & fun · Sep 2026 · rivendell.dmitrybrant.com

  • Jev▲544

    Fast, structured AI decisions for software automation

    AI · Sep 2026 · console.typesafe.ai

  • ReSO AI▲510

    Get discovered & recommended when customers ask AI

    AI · Sep 2026 · resollm.ai

  • Start coding agents, control sessions, review PRs. Anywhere.

    Dev tools · Sep 2026 · kilo.ai

  • AINA▲497

    Find your job search blind spots with an AI coach

    AI · Sep 2026 · aina-tech.io