TechnologyEvolution.AI @TimothiousAI

@MatthewBerman We all knew it was coming. Protect your data everyone, the race is on... Next stop AGI

I just gotta say, so glad GPT-5.5 (spud) was released. All my OpenClaws finally acting like they did when they were on Opus 4.7. Huge improvement over 5.4 in personality and long horizon tasks. This is the first time @OpenAI really hit the mark for me.

@49agents It depends on the use case. I specify my context window / kv cache depending on what the workflow requirements. 64k is usually plenty

When OpenClaw shut down I had two choices: wait for a replacement or build my own agent harness on Claude Code. I spent 3 days building it. 13 phases. 45+ PRs. It's now more capable than what I had before. Here's the architecture if you want to do the same:

Total cost: under $200/month running on a single workstation with local inference (35B model for bulk work, 8B for classification, cloud API for orchestration and review). If you were on OpenClaw and you're looking for what's next, this is it. Claude Code + Agent SDK + the Second Brain pattern from the Dynamous.ai community. Everything you need is already there. Follow me for more agentic processes pushing the limits of whats capable with today's technology. #technologyevolutionai

The part nobody talks about: the system improves itself. Every harness run captures a trajectory (what worked, what failed, which model was used). A weekly pipeline analyzes batches of trajectories and generates evolved skill files. Your agent literally gets better at its job every week without you touching the code (or whenever you want)!

I built this into my AI Second Brain today. Karpathy is right, at personal scale, a well-maintained index.md beats vector search. The LLM just reads it and knows where to look. The part nobody's talking about: this same pattern scales. Add hybrid RAG at 500+ docs and it becomes an enterprise knowledge system.

Andrej Karpathy @karpathy

4 months ago

LLM Knowledge Bases Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating

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Can someone tell Anthropic it’s not Christmas… At least the gov service is green. Sheesh

Software horror: litellm PyPI supply chain attack. Simple `pip install litellm` was enough to exfiltrate SSH keys, AWS/GCP/Azure creds, Kubernetes configs, git credentials, env vars (all your API keys), shell history, crypto wallets, SSL private keys, CI/CD secrets, database passwords. LiteLLM itself has 97 million downloads per month which is already terrible, but much worse, the contagion spreads to any project that depends on litellm. For example, if you did `pip install dspy` (which depended on litellm>=1.64.0), you'd also be pwnd. Same for any other large project that depended on litellm. Afaict the poisoned version was up for only less than ~1 hour. The attack had a bug which led to its discovery - Callum McMahon was using an MCP plugin inside Cursor that pulled in litellm as a transitive dependency. When litellm 1.82.8 installed, their machine ran out of RAM and crashed. So if the attacker didn't vibe code this attack it could have been undetected for many days or weeks. Supply chain attacks like this are basically the scariest thing imaginable in modern software. Every time you install any depedency you could be pulling in a poisoned package anywhere deep inside its entire depedency tree. This is especially risky with large projects that might have lots and lots of dependencies. The credentials that do get stolen in each attack can then be used to take over more accounts and compromise more packages. Classical software engineering would have you believe that dependencies are good (we're building pyramids from bricks), but imo this has to be re-evaluated, and it's why I've been so growingly averse to them, preferring to use LLMs to "yoink" functionality when it's simple enough and possible.

Daniel Hnyk @hnykda

4 months ago

LiteLLM HAS BEEN COMPROMISED, DO NOT UPDATE. We just discovered that LiteLLM pypi release 1.82.8. It has been compromised, it contains litellm_init.pth with base64 encoded instructions to send all the credentials it can find to remote server + self-replicate. link below

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@MatthewBerman 😂

@andyreed ok - I have had a good laugh out loud moment in a while. Not at the actual poor bloke who got hurt, but at all the subagent experiences I've had so many times this is the case. Claude shipped agent teams (experimental) but its the right direction.

+1 for "context engineering" over "prompt engineering". People associate prompts with short task descriptions you'd give an LLM in your day-to-day use. When in every industrial-strength LLM app, context engineering is the delicate art and science of filling the context window with just the right information for the next step. Science because doing this right involves task descriptions and explanations, few shot examples, RAG, related (possibly multimodal) data, tools, state and history, compacting... Too little or of the wrong form and the LLM doesn't have the right context for optimal performance. Too much or too irrelevant and the LLM costs might go up and performance might come down. Doing this well is highly non-trivial. And art because of the guiding intuition around LLM psychology of people spirits. On top of context engineering itself, an LLM app has to: - break up problems just right into control flows - pack the context windows just right - dispatch calls to LLMs of the right kind and capability - handle generation-verification UIUX flows - a lot more - guardrails, security, evals, parallelism, prefetching, ... So context engineering is just one small piece of an emerging thick layer of non-trivial software that coordinates individual LLM calls (and a lot more) into full LLM apps. The term "ChatGPT wrapper" is tired and really, really wrong.

tobi lutke @tobi

a year ago

I really like the term “context engineering” over prompt engineering. It describes the core skill better: the art of providing all the context for the task to be plausibly solvable by the LLM.

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#BREAKING: Another $5 BILLION investment fund registered in an overseas tax haven by Mark Carney has been uncovered, this time in the Cayman Islands.

Great way to retain context while working with LLM on a larger projects.

Dan McAteer @daniel_mac8

a year ago

🤯 this genius stores his entire codebase syntax in a graph database and queries it so provide context to an llm

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18 Deep Visuals about Psychology and Life: 1.

You should do one for 10 year + professional developers only to answer. Cursor has obviously done a great job on advertising, but recently their 3.7 sonnet roll out was an absolute disaster. Having 3.5 take over a project for vibe coding breaks everything. Would want to hear what the professionals thing.