rvaniaaa
@rvaniaaaa • 3,304 subscribers
Markets. Systems. Things people overlook. Explaining AI workflows & systems.
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I still don't understand why everyone is still running agents in a line. I switched to graphs three weeks ago and my fleet finished in the time my single agent used to spend on step two. what slows every agent system I have seen is not intelligence. it is geometry. and almost nobody is talking about it. one engineer used this to rewrite 535,000 lines of code in 11 days. a manual rewrite of that scale could take close to a year. it cost $165,000 in tokens. the graph was not cheap. it was just faster than a human year. a node is one agent with one job. research one competitor. review one file. check one claim. the moment a node owns two independent jobs you lose the ability to parallelize them cleanly, verify them independently, and debug them in isolation. an edge is a dependency. it only exists when data actually moves across it. everything else is a fake edge. a wait you invented that costs time and produces nothing. find the fake edges and the line collapses into something wider. jobs that can run at the same time run at the same time. what used to take the sum of forty steps now finishes in the time of the slowest layer. the pattern behind every serious agent system looks like a diamond. fan out to gather breadth, one agent per angle, all at once. reduce with plain code, no model tokens spent. verify with a fresh skeptic on every finding. synthesize once from what survived. Claude's own research feature uses a very similar pattern in production. the part nobody warns you about: the verifier needs clean context. give it the same conversation the worker had and it is not checking anything. it is nodding along to itself in a different window. a graph of agents sharing one context is a single loop in a costume. it breaks the same way, just later and more expensively. one rule that holds at every scale. a worker and its verifier must never share a context. your agents are not too slow. they are waiting in a line that did not need to exist. full guide in the article. save it before you build your next agent from scratch.
rvaniaaa256,562 Aufrufe • vor 12 Tagen
Jev just became the fastest-adopted model in AI history. here's what people already built with it 1. jev-ultrafast - the browser agent that picks every click itself, only calling a text model when it actually needs to type something. found real flight results in 7 seconds for $0.0039. 16,758 stars 2. jev-trader - real trading bot placing live limit orders on Monad every 300ms block, judged by Jev alone. 1,911 stars 3. jev-usecases - production security-operations harness where Jev triages incidents and gates every escalation behind a confidence cutoff before anything touches real infrastructure. zero false escalations in the committed test set 4. tax-doc-classifier - sorts real IRS tax forms with 100% strict accuracy across 261 forms, at roughly $0.001 a page 5. jev-drone - a simulated quadrotor clears a five-station obstacle course by camera alone, Jev judging the situation twice a second 6. killmyidea - describe your startup idea, Jev scores it from every angle, then hands back kill, fix, or ship in seconds, not days 7. jev-curate - streams Parquet and JSONL rows through typed judgments at 1,500+ rows a second, keeping only what clears the bar 8. pg-jev - a PostgreSQL extension that lets you ask your own database tables plain-English questions and get a real answer back, no SQL required eight repos. zero generated words. every single one returns a typed answer against a question someone already defined full setup below, then run the three-question test from the article before you build a ninth
rvaniaaa85,773 Aufrufe • vor 8 Tagen
I still don’t understand why everyone isn’t running one of these yet. mine has been compiling for six months and it already knows things I’ve forgotten I knew Andrej Karpathy published a folder structure in April 2026. 5,000 stars. 16 million views. engineers couldn’t stop sharing it. not a model, not a tool, a folder structure. the sentence that explains why: “RAG re-derives knowledge on every query. a compiled wiki derives it once and keeps it current” most people who try building one hit a wall around month three anyway. the notes pile up, the links exist, and then it just sits there. you’re still the one doing all the remembering here’s the sentence that explains that wall too: keeping fifty interlinked notes current, consistent, and cross-referenced is work no human sustains. so people stop maintaining it, and it turns into a graveyard of good intentions the fix isn’t discipline. it’s making the system do the maintenance instead of you here’s the gist drop any source into a folder called raw, untouched, ground truth Claude reads it once, writes a structured page into wiki, links it to everything already there, flags anything that contradicts what you already know one file called CLAUDE.md sits at the center holding who you are, so you stop re-explaining yourself every session ask it anything across everything you’ve ever fed it, and it answers from months of compiled understanding instead of starting from zero that loop is the whole system. Karpathy’s repo got 16 million views because engineers finally saw the difference written down: retrieval answers questions, compilation builds understanding five minutes to set up. you’ll never open an empty chat and re-explain your life again full build, every step, in the article. save it now
rvaniaaa60,398 Aufrufe • vor 27 Tagen
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