For months I've been frustrated with LLMs ability to get stuff done. But they're weren't always at fault - most of the time they just lack context. However no AI IDE currently give you ability to manage context. This changes TODAY. Download for free at conare.ai
@LiorOnAII've tried all ( 74 😵💫 ) AI Coding Agents & IDEs
[Rork, CodeRabbit, Anima, Zed, Factory, Cursor, Windsurf, Copilot, Lovable, Bolt, v0, Replit, MarsX, Canva, Devin, Github Spark, Vercel, Lindy, Warp, Figma, Cline, Vibe Coder & more]
The most complete list ever made (with demos & notes):@johnrushx
@bbssppllvv
▶@coderabbitai
@dhruvtwt_Top 5 local LLMs:
1. GLM-4.5-air: best agentic/coding model that runs on consumer hardware at very decent speeds. Rivals Claude 4-sonnet.
2. Nousresearch/hermes-70B: the only model that will do whatever you ask, and tell you whatever you want to know. Literally critical to have.
3. GPT-OSS-120B: very intelligent it’s like having 4o at home, great context window, great agent
4. Qwen3-coder-30B-3A-instruct: very good coding agent, excellent workhorse, incredibly fast
5. Mistral-magistral-small: very fast, excellent agent, great coder, multimodal, punches way, way, above its size.
I would be okay never using a proprietary llm, although given the subsidised compute I will continue to use them since I’m getting free leverage.@0xSero
▶@bflycomputer
@tom_doerr
@chrstnerode≡ 11+11
@every
▶@abouelatta_aliAfter a couple hours of work, I finally finished developing my first ever skill. :D
Claude’s frontend skill tells the AI to "pick an extreme aesthetic" and "be creative."
The problem tho is LLMs are just based on probability. Without strict rules, they statistically default to the most likely patterns, that's where AI slop comes from.
To get clean and production-grade UI, you need to override these biases with some engineering constraints.
I open-sourced Taste-Skill to fix this. :)
Check it out! https://github.com/Leonxlnx/taste-skill
(still early lots of improvements are on the way)@LexnLin
▶@prathamgrv
@PrajwalTomar_I took the @karpathy autoresearch loop and pointed it at markets.
25 AI agents debate macro, rates, commodities, sectors, and single stocks daily. Every recommendation scored against real outcomes. Worst agent by rolling Sharpe gets its prompt rewritten by the system. Keep or revert. Same loop, prompts are the weights, Sharpe is the loss function.
Trained the agents on 18 months of market data. 378 iterations. 54 prompt modifications, 16 survived.
The system learned which agents to trust using Darwinian weights — geopolitical, commodities, and the @BillAckman quality compounder rose to the top.
The agents even figured out their own portfolio manager was the weakest link before we did!
Deployed the trained agents. +22% in 173 days. Best pick: AVGO at $152, held for +128%.
The final prompts are evolutionary products — shaped by market feedback, not human intuition. Now running live with my own capital.
https://github.com/chrisworsey55/atlas-gic
Part hedge fund, part research experiment :)@Chris_Worsey
@Dr_Singularity
@amilabs
▶@toolfolioAnnouncing Paper Desktop + MCP
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