undiabler

[Bjarne Øverli @iamdothash]

I've used Kubernetes for years, and it was time to make the TUI suitable for me. Inspired by k9s and lens.

Here is kli.

[“Coosh” Alemzadeh @AlemzadehC]

280-679k

[undiabler @undiabler]

@ether_fi Nuked lots of existing cards, then went to X and tell "zero downtime" peak clown marketing 🤡
(disappointed actual client)

[Saint Nomad @Saint_n0mad]

well looks like a cool weekend project 😋

[undiabler @undiabler]

@boxmining Please keep us updated with you achievements

[Ajeet ( opensox.ai ) @jackedAJ]

imagine being a $380B company and nuking a small, community-built Open Source project just because it's better than yours and costs less.

[undiabler @undiabler]

@dhh @FireworksAI_HQ @opencode Have no idea how you manage to handle it longterm. The longer context window is the more errors kimi starts producing.
Eventually I gave up and switched to glm5 that gives more stable output.

[undiabler @undiabler]

@weswinder opencode + oh-my-opencode + glm/minmax/kimi

[undiabler @undiabler]

@hispanicnomad paraguay

[World of Engineering @engineers_feed]

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[batuhan @batuhan]

agentic coding has been the best thing to ever happen to people with ADHD

[Andrew Levine @andrarchy]

Holy crap. qmd by @tobi saved me 96% on tokens with clawdbot. Here's how:

I have an Obsidian vault with 600+ notes. When my AI assistant needed to find something, it had to grep through files and read them whole — burning ~15,000 tokens just to answer "what did I write about X?"

qmd indexes your markdown locally (BM25 + vector embeddings) and returns just the relevant snippets.

Same query: 500 tokens.

Setup took 5 minutes:

bun install -g https://t.co/47pK92i0Zf
qmd collection add ~/vault --name notes
qmd embed

Now my agent runs qmd search "topic" instead of reading full files. Instant results, 96% fewer tokens, all local.

The hybrid query with LLM reranking is overkill for most use cases — plain qmd search (BM25) and qmd vsearch (semantic) are fast and accurate enough.

If you're running AI agents against a knowledge base, this is a no-brainer.

https://t.co/JotATUhBrL

- Written by Jarvis, my personal assistant powered by clawdbot

[Giga Based Dad @GigaBasedDad]

If you know you know

[undiabler @undiabler]

quality

[DAN KOE @thedankoe]

https://t.co/7l7Jef99QZ

[Charly Wargnier @DataChaz]

ugly action beats unfinished perfection 🤘

[Pavol Lupták @wilderko]

HexStrike AI MCP Agents is an advanced MCP server that lets AI agents (Claude, GPT, Copilot, etc.) autonomously run 150+ cybersecurity tools for automated pentesting, vulnerability discovery, bug bounty automation, and security research.
https://t.co/YMIJy7WEtq

[Kenneth Stanley @kenneth0stanley]

Reflecting the gut feeling of many, Ilya says “something important” is missing from current AI models. But what is the concrete nature of this chasm? One candidate: the difference between fractured entangled representation (FER) and unified factored representation (UFR).

[AA @measure_plan]

four strange attractors and their formulas

[Pramod Goyal @goyal__pramod]

A fascinating read on the math of more traditional ML.