Steve Solun @SteveSolun
AI Expert. CTO. Entrepreneur. Connector. Visioner. Out-of-the-Box Thinker linkedin.com/in/solunsteve/ Joined October 2019-
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@mattpocockuk @zeeg I tried your skills, waste of time for really deep challenges. You must use the recommendation layer that includes many tools and load/unload the tools, depending on the task. github.com/stevesolun/ctx
@zeeg Try my recommendation layer for LLMs: github.com/stevesolun/ctx
@mattpocockuk Meaning that within this plugin your skills will be up to date on every change? I want to use it in my recommendation layer: github.com/stevesolun/ctx
The workflow: Observe real runs → identify repeated tasks → move predictable work from model calls into ordinary code → keep the LLM for judgment and reasoning → benchmark quality, cost, and latency. Read the full guide: gist.github.com/stevesolun/7f5…
Most AI agents use expensive model calls for work that should be deterministic: fetching, parsing, filtering, retries, and state. Profile real runs. Keep the LLM where reasoning creates value. Compile everything else into software. Full practical guide ↓
Releasing the model weights and technical report of Kimi K3. Kimi K3 is our most capable model: a 2.8T MoE model with native visual understanding and a 1M-token context window. New model architecture: 2.5x the intelligence per unit of compute, not just more params. Alongside Kimi K3, we're opening up more of the stack behind it — high-performance attention kernels, MoE communication library, and infrastructure for running agent environments at scale. Model weights: huggingface.co/moonshotai/Kim… Tech report: github.com/MoonshotAI/Kim… Tech blog: kimi.com/blog/kimi-k3
@sama You must use the recommendation layer for LLMs: github.com/stevesolun/ctx
@miramurati @lilianweng Try the recommendation layer for llms: github.com/stevesolun/ctx
@LuminaXspace It can be integrated with ctx recommendation layer: github.com/stevesolun/ctx
@mattpocockuk As usual you miss the recommendation layer.... github.com/stevesolun/ctx
Introducing Claude Opus 5. It's a thoughtful and proactive model that comes close to the frontier intelligence of Fable 5 at half the price.
@mattpocockuk It doesn't really work for real hard problems. It can work if it will be part of a recommendation layer: github.com/stevesolun/ctx
My way of doing things
One pattern I find useful for working with LLMs is a nice long ramble session. Sometimes the LLM needs more bits to understand what you're trying to achieve, but you're too lazy to type them. In these cases I like to lean back, switch to /voice and just ramble for like 10
@mattpocockuk Dude, chill, it's waste of time.... Just use the recommendation system for LLMs: github.com/stevesolun/ctx
Someone open sourced the entire recipe for a personal AI computer. Not software, the hardware: parts list, CAD, BIOS settings, assembly photos. 4 builds, from 2x RTX 5090 (64GB VRAM) up to 4x RTX PRO 6000 (384GB). MIT license, fork it and sell it. github.com/autonomous-ai/…
@mattpocockuk BS! It will work only if you use a real recommendation layer for LLMs: github.com/stevesolun/ctx
@mattpocockuk Must have chapter in that course should be the recommendation layer for LLMs that will load and unload only the necessary/unnecessary tools. github.com/stevesolun/ctx
@FeedTechILUncen אכן מה שמתפספס זה מה שבניתי: כלי שממליץ על כמות מצומצמת של כלים בכל תקופת פיתוח ואז מוריד מה שלא צריך. github.com/stevesolun/ctx
Proudly representing #Israel at the Prague Folklore Festival! 🇮🇱 We marched with our national flag and received so much warmth and love from people visiting #Prague. What an incredible, unforgettable experience! A wow moment! @naftalibennett @gadi_eisenkot @netanyahu @AvigdorLiberman
Woooow
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