Alternatives
Products that do what DetectPack Forge does
Turn your words into a rule maker.
- 1FG
Hi HN, I'm Antoine Zambelli, AI Director at Texas Instruments. I built Forge, an open-source reliability layer for self-hosted LLM tool-calling. What it does: - Adds domain-and-tool-agnostic guardrails (retry nudges, step enforcement, error recovery, VRAM-aware context management) to local models running on consumer hardware - Takes an 8B model from ~53% to ~99% on multi-step agentic workflows without changing the model - just the system around it - Ships with an eval harness and interactive dashboard so you can reproduce every number I wanted to run a handful of always-on agentic systems…
May 2026 · github.com
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- 5KS
Hi All, Recently I've been using Claude Code a lot for debugging cluster issues and I realized I was performing similar tasks repeatedly so I decided to package them up into skills so I could call them up more easily (e.g. `/investigate`, `/audit-security`, `/audit-outdated`). I'm calling the skill pack "kstack" and the goal is to be able to monitor and troubleshoot K8s from within Claude Code. If you have time, I'd love to get some feedback on the project! Andres Source: https://github.com/kubetail-org/kstack Docs: https://kstack.sh/
May 2026 · github.com
- 6PD
2021 · pickbetterpack.com
- 7KR
I've spent the past few years building 50+ AI agents in prod (some reached 1M+ sessions/day), and the hardest part was never building them — it was figuring out why they fail. AI agents don't crash. They just quietly give wrong answers. You end up scrolling through traces one by one, trying to find a pattern across hundreds of sessions. Kelet automates that investigation. Here's how it works: 1. You connect your traces and signals (user feedback, edits, clicks, sentiment, LLM-as-a-judge, etc.) 2. Kelet processes those signals and extracts facts about each session 3. It forms hypotheses…
Apr 2026 · kelet.ai
- 8AM
I built an open-source AIOps MCP (Monitoring & Control Plane) that detects anomalies in logs using Isolation Forest. It accepts logs from agents, apps, or collectors, parses and extracts features, and identifies unusual patterns in real time. Alerts can be sent to Slack, Webhooks, or PagerDuty. It’s lightweight, easy to deploy with Kubernetes & Helm, and designed to plug into existing observability stacks. I built this to experiment with combining ML-based anomaly detection and flexible alerting for DevOps/SRE teams. Most AIOps platforms are either too heavyweight or closed-source — I…
2025 · github.com
- 9KD
I built this after seeing multiple teams accidentally ship API keys in their frontend code. The problem: Modern web development moves fast. You're vibe-coding, shipping features, and suddenly your AWS keys are sitting in a tag visible to anyone who opens DevTools. I've personally witnessed this happen to at least 3-4 production apps in the past year alone. KeyLeak Detector runs through your site (headless browser + network interception) and checks for 50+ types of leaked secrets: AWS/Google keys, Stripe tokens, database connection strings, LLM API keys (OpenAI, Claude, etc.), JWT…
Nov 2025 · github.com
- 10UA
I've been using LLMs for long discovery and research chats (papers, repos, best practices), then distilling that into phased markdown (build plan + tests), then handing those phases to Codex/Claude to implement and test phase by phase. The annoying part was always the distillation and keeping docs and architecture current, so I built Unpack: a lightweight GitHub template plus docs structure and a few commands that turns conversations into phases/specs and keeps project docs up to date as the agent builds. It can also generate Mintlify-friendly end-user docs. There are other…
Feb 2026 · github.com
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Hi HN, I'm Kaushik, and I built Rocketgraph. I believe that while other spaces have caught up to the AI wave, the observability space is still lagging behind, using the same tools and dashboards that we use to analyse logs from human-written code. But now the code is written and debugged by AI, so we need to rethink how we do observability where the observer itself is an AI. The problem that I run into is when an alert fires, I have to manually check the Grafana dashboards and write LogQL queries, which is pretty much like greping. But production usually breaks due to a schema mismatch, or a…
Jun 2026 · github.com
- 12IB
2021 · log4shell.tools
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Hi HN! We’re Ethan and Danny, the authors of Tangent (https://github.com/telophasehq/tangent), a Rust-based log pipeline where all normalization, enrichment, and detection logic runs as WASM plugins. We kept seeing the same problems in the OCSF (https://ocsf.io) community: 1) Schemas change constantly. Large companies have whole teams dedicated to keeping vendor→OCSF mappings up to date. 2) There’s no shared library of mappings, so everyone recreates the same work. 3) Writing mappers is tedious, repetitive work. 4) Most pipelines use proprietary DSLs that are…
Nov 2025 · github.com
- 14HW
Hello everyone! I’m thrilled to announce the latest feature from Mutahunter.ai, the ultimate tool for finding and fixing weaknesses in your code. We’ve designed Mutahunter to leverage mutation testing powered by advanced LLMs, helping you uncover vulnerabilities and enhance your code quality effortlessly. Introducing our newest feature: Detailed Mutation Testing Reports! After running our mutation tests, Mutahunter now generates comprehensive reports that clearly summarize: • Vulnerable code gaps • Test case gaps These reports significantly reduce the cognitive load on developers by…
2024 · github.com
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If you are worried about the recent Lazarus group software supply chain attack, you should consider having guard rails that is more than conventional SCA. `vet` detects the package (version) published in the report as malware. Try out vet, its free and open source: https://github.com/safedep/vet More details on the attack: https://www.nodejs-security.com/blog/north-korea-malware-on-...
2023 · github.com
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2024 · github.com
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Hi HN, I'm Robel. I built LogClaw because I was tired of paying for Datadog and still waking up to pages that said "something is wrong" with no context. LogClaw is an open-source log intelligence platform that runs on Kubernetes. It ingests logs via OpenTelemetry and detects anomalies using signal-based composite scoring — not simple threshold alerting. The system extracts 8 failure-type signals (OOM, crashes, resource exhaustion, dependency failures, DB deadlocks, timeouts, connection errors, auth failures), combines them with statistical z-score analysis, blast radius, error velocity, and…
Mar 2026 · logclaw.ai
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2025 · github.com
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We open-sourced the TLA+ and Fizzbee verified spec behind Ursa's storage engine. Verification across ~200K states caught a design bug that years of production missed. We then handed the spec to Claude Code — it produced a working Rust implementation (concurrent producers, compaction, fencing) without back-and-forth. We think verified specs are the best harness for coding agents: open-source the spec, let anyone implement it.
Apr 2026 · github.com
- 21TD
Trusty - Search for an open source package to understand its trustworthiness based on activity, provenance, and more. Brought to you by the founders of projects such as Kubernetes and Sigstore. Hey, Luke here the CTO of stacklok. This is an early experimental preview of Trusty. We use statistical analysis to observe millions of packages and found that Malware typically follows certain patterns. We found this tool really useful to help understand the packages we our pulling into our software and wanted to share it with others. It's still early in and we have a lot more features that will be…
2023 · trustypkg.dev
- 22CA
Synthetic data generation is an essential step in training and evaluating LLMs/Agents/RAG pipelines, but tooling around this is still lacking. We're introducing Curator, an open-source library designed to streamline the data curation process. While there are many libraries to prompt LLMs, the semantics of generating synthetic data is different from prompting. For example, we need to process a large number of prompts (sometimes in millions or more) while accepting some failures, utilize several stages of prompting, incorporate human feedback, and filter out bad data using verifiers…
2025 · github.com
- 23IB
Hi! My name is Herve Kom, a computer science student that is interested in learning new things everyday! As one of my graduation project, I have developed a Claude Code -like Coding CLI, but with enhancement for API Testing: - Auto-generate & run tests (unit, e2e, Playwright, CI/CD, etc.) - Say bye-bye to hallucinations with built-in MCP Server to let LLM directly read from API Docs - Adding Agent.md support for better context persistence across your whole codebase - Automatic bug & security scans (logic is kind of basic but works great!) - Vibes, I want it to feel less "enterprise" but…
2025 · github.com
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