Annatar.md
@AnnatarXBT • 1,548 subscribers
I have seen many fail with crude prompts Let me teach you the hidden ways of Claude Skills, agents, and lectures
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NVIDIA is giving away free access to 140+ AI models for a full year > a lot of people building agents are paying $50-200/month just for API access NVIDIA just made that argument irrelevant models you get: GLM 5.2, MiniMax M3, Nemotron-3.5-lightning-30b-a3b, Muse-glimmer-30b from Meta and 130+ more setup: > step 1 - get your free key > go to > register -> bind phone -> copy API key > step 2 - add to Hermes agent > open Settings -> Model Provider -> Custom base_url = " api_key = "nvapi-xxxxxxxxxxxxxxxxxxxx" > step 3 - pick a model model = "z-ai/glm-5.2" model = "minimaxai/minimax-m3" model = "nvidia/nemotron-3-ultra-550b-a55b" model = "moonshot-ai/kimi-k2.7" > Hermes already has NVIDIA set as default base_url > paste the key and you're running instantly > works the same in Cursor and OpenCode > cost: $0 > limit: 40 req/min > expires: 1 year a free key changes what you can afford to run: > one prompt costs nothing either way > a loop firing 300 agents every week is where the bill shows up > for a year that bill is zero while everyone is paying for API access, this is sitting there for free with this many models in one place it is also the easiest way to test which one actually fits your agent before you commit to a paid provider
Annatar.md205,588 просмотров • 7 дней назад
Google's team published a 9-page PDF on Harness Engineering. one formula replaced prompt engineering: Agent = Model + Harness the twist: same Claude Sonnet, same benchmark. change only the harness the playbook, six steps: step 1 -> add guides AGENTS.md, rule files, constraint docs. every line is a past agent failure turned into a permanent fix step 2 -> add sensors linters, tests, validation scripts the agent runs on its own output before a human sees it step 3 -> build the agentic loop plan, execute, verify, fix. bounded retries, budget caps, escalation when stuck step 4 -> externalize memory the model forgets every session. the harness holds state, decisions and artifacts across all of them step 5 -> enforce permissions which tools, how many writes, what needs approval. safety lives in the harness, never in the model step 6 -> wire observability track every tool call, cost and retry. trip wires fire when behavior drifts the result: your agent turns from a demo into infrastructure every failure makes the system permanently better, not just the next conversation that's also the line between an agent you show people and one a client pays you to leave running one harness gets you a reliable loop. connect several and it becomes graph engineering, where one agent's failure turns into another agent's sensor the PDF stops at the harness. the graph is the next layer that breakdown, running on Kimi, is below ↓
Annatar.md72,865 просмотров • 3 дней назад
this is free f*cking gold Andrej Karpathy joined Anthropic to lead a team pointing Claude at Anthropic's own pretraining research a model helping design the model that comes after it. that part is confirmed the line going around - "two Anthropic seniors made his loop 1000x better with graph engineering" - nobody can source it, so I'm not selling it as fact what is real and public: Anthropic's own Claude cookbook on knowledge graph construction > extract - pull entities and claims out of raw text > resolve - decide which mentions point to the same thing > assemble - connect them with typed edges > query - ask questions no single document could answer four steps, free, sitting in the repo while the timeline quotes a rumor learn them this weekend and you can build the thing the rumor was describing - that skill is what gets handed the AI architecture work this year read the cookbook first. then decide if the article below earned the click
Annatar.md91,874 просмотров • 4 дней назад
this is f*cking gold an internal AI engineering document leaked. it's saving solo devs $300,000 a year that number is just the three hires you never make: the one who writes the spec, the one who reviews the output, the one who runs the queue prompt-driven is out. loop-driven is in. that shift changes everything Generate → Evaluate → Remember → Schedule → Optimize → Recurse six layers, one loop. it improves itself. no human in the middle > generation - writes its own brief, then builds against it > evaluation - a second layer grades the work and hands it back > memory - keeps what worked, so the same mistake costs you once > scheduling - picks the next job. the queue runs itself > optimization - rewrites its own instructions from what shipped > recursion - pull one layer out and the whole thing degrades that last one is the tell. six layers is the floor, not the wishlist the human moves from operator to architect AI stops predicting the next token. it starts running its own loop the people who wire this up in a weekend spend next year reviewing output. everyone else keeps typing prompts
Annatar.md44,808 просмотров • 5 дней назад
this is pure f*cking gold andrej karpathy landed at anthropic 14 weeks ago about that "two anthropic seniors made his loop 1000x better with graph engineering" line - i can't find a source for it anywhere, so i'm not passing it off as fact what is public and checkable: anthropic's own cookbook on building knowledge graphs, plus karpathy's autoresearch loop that ran 700 experiments in 2 days and surfaced 20 optimizations on its own i wired the graph approach into my own setup the difference showed up on the very first reply claude skipped the stock answer and actually reasoned through the problem start with the cookbook, the full write-up is below
Annatar.md46,855 просмотров • 6 дней назад
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