Rhoxbt

AI researcher & agent builder | Content creator

[Rhoxbt @Rhoxbt]

WHOEVER LEAKED THIS JEV CELL ATLAS SHOULD BE WRITING THE TEXTBOOK

I thought surface-to-volume ratios were a textbook exercise with made-up spherical cells and a page of worked examples.

Apparently not.

This atlas's setup is much more interesting:

→ GEOMETRY computes V = (4/3)·π·r³ and A/V = 3/r per organelle straight from the segmented voxels, so a mitochondrion at 1.3 μm radius carries its own volume and surface, no textbook average
→ SEGMENT runs unet3d on the cell volume and asserts dice above 0.94 before anything gets counted, no silent "trust the model" step
→ POTENTIAL solves the Nernst equation from the Na+/K+ pump ratio 3:2 and returns -66.8 mV in real time, no table lookup, no "use the normal range"
→ THROUGHPUT tracks live rates per compartment (protein synthesis 11,717 aa/s, ATP 33.5 mol/s), each logged against its own baseline, no snapshot bias
→ DIMENSION chart logs the typical size per organelle in log scale at 1,439 samples per second (plasma membrane 0.004 μm, ribosome 0.020, peroxisome 0.2, nucleus 5.6, cytoskeleton 9.8), flags 7 organelles drifting past the atlas baseline this scan

so you get:

Geometries computed. Volumes segmented. Potentials solved. Rates logged.

the interesting part isn't segmenting more cells.
it's giving every organelle a geometry the next cell gets compared against.

Instead of a textbook mitochondrion at some generic radius, you load a mammalian cell, assert dice above 0.94, and read Cytosol 55.9, Mitochondria 22.8, Rough ER 9.0, Nucleus 5.8 as actual measured volume fractions.

Compute the geometry. Segment the volume. Solve the potential.

[codila @0xCodila]

Jev is the "Internet" moment for the AI industry

It tells your agents and LLMs what to do next, in milliseconds and at almost zero cost

If you set it up correctly, you will have the AI engineer’s stack for 2028

In this article, I show you how https://t.co/x3qn41ejnr

[Rhoxbt @Rhoxbt]

WHOEVER LEAKED THIS JEV TOROIDAL CORE DESERVES A PATENT

I thought agent routing was a software problem you fixed by prompting the model better.

Apparently not.

This core's setup is much more interesting:

→ CHANNEL REGISTRY exposes all 8 lanes with live phase and load (CH-01 phase 1.769 load 52, through CH-08 phase 2.749 load 64), no hidden channel, no "whichever is free"
→ ROUTING LOGIC runs every signal through INPUT → VALIDATE → SELECT → DISPATCH → REVIEW → MONITOR, a bad packet fails at validate not at review three steps later
→ TOROIDAL ASSEMBLY wraps outer shell, phase bridge, inner channel and core array into one closed flux path, no stray voltage leaks out across the boundary
→ PHASE MONITOR tracks CH-01 against CH-03 in real time, locks when sync state crosses baseline, drops when phase drifts past 0.004 rad resolution
→ EVENT TRACE logs every dispatch, phase_lock, buffer_flush and route_select with timestamp and channel id, flags 7 phase drifts the monitor caught before the reviewer did

so you get:

Channels registered. Routes validated. Phases locked. Traces committed.

the interesting part isn't making the signal faster.
it's giving every packet a lane whose phase the monitor already knows.

Instead of a software router that eventually ships the wrong packet to the wrong worker, you route 256 nodes across 6 of 8 active channels at 12.483 kHz, sync state LOCKED, core temp 42.4, nothing selected without a phase check.

Register the channel. Validate the route. Lock the phase.

[codila @0xCodila]

Jev is the "Internet" moment for the AI industry

It tells your agents and LLMs what to do next, in milliseconds and at almost zero cost

If you set it up correctly, you will have the AI engineer’s stack for 2028

In this article, I show you how https://t.co/x3qn41ejnr

[Rhoxbt @Rhoxbt]

WHOEVER LEAKED THIS JEV GRID WITH THE SPLIT SHOULD BE TEACHING THIS AT STANFORD

I thought good agents were about picking the smartest model and letting it do everything in one turn.

Apparently not.

This grid's split is much more interesting:

→ LLM only writes: it drafts prose and nothing else, no routing, no deciding what the next step is, no "let me think about where this goes"
→ JEV only decides: Choice, Score, Noul questions against the shared state, output tokens literally zero, 256ms median per decision
→ CODE only acts: calendar invites fire, mail moves to quarantine, clicks run with outcome checks, no model wake-up anywhere in the loop
→ ONE STATE is shared across all three roles, 15 walls and 600 cards indexed, no cross-role leaks, every role reads the same world
→ GATE enforces 0.85 between Jev and code, drops below to review, fires above, flags 6 cases where one role tried to do another's job this run

so you get:

Writers write. Deciders decide. Acts act. States shared.

the interesting part isn't picking the smartest model.
it's giving every role one job the other two can't do for it.

Instead of one model doing writing, deciding and acting in one freeform turn, you split them: LLM writes, Jev decides, code acts, with 10,000 decisions at $0.42, 256ms median, and gate 0.85 holding every handoff.

Write with the LLM. Decide with Jev. Act in code.

[codila @0xCodila]

Jev is the "Internet" moment for the AI industry

It tells your agents and LLMs what to do next, in milliseconds and at almost zero cost

If you set it up correctly, you will have the AI engineer’s stack for 2028

In this article, I show you how https://t.co/x3qn41ejnr

[Rhoxbt @Rhoxbt]

WHOEVER LEAKED THIS JEV TRADING DESK DESERVES THE QUANT CHAIR

I thought running a crypto bot meant one script, one API key, and a stop-loss you check every morning.

Apparently not.

This desk's setup is much more interesting:

→ WATCHER streams BTC, ETH, SOL in parallel at 1.4M packets per second, 12ms end-to-end, pure push, no polling
→ ANALYST fuses spot, futures and options into one 24×24 evidence matrix with per-market confidence (0.84, 0.90, 0.77), no single-market blindspot
→ RISK clamps net exposure before the executor touches the book, holds the handoff queue at 65% headroom, no breach on burst
→ EXECUTOR fills through 3 hops at 91% handoff rate, every fill tagged against the signal that triggered it
→ REPORTER keeps a 0.85 confidence floor and routes anything below to the human operator, flags 7 signals the six operators disagreed on this hour

so you get:

Signals streamed. Markets fused. Exposure clamped. Fills tagged.

the interesting part isn't adding more alpha.
it's giving every signal a desk six operators have to sign before it fills.

Instead of one bot running one strategy on one feed, you fuse six markets into one evidence matrix, route through 3 hops at 12ms, and let the 0.85 floor kick anything shaky to the human.

Fuse the markets. Clamp the risk. Tag the fill.

[codila @0xCodila]

Jev is the "Internet" moment for the AI industry

It tells your agents and LLMs what to do next, in milliseconds and at almost zero cost

If you set it up correctly, you will have the AI engineer’s stack for 2028

In this article, I show you how https://t.co/x3qn41ejnr

[Rhoxbt @Rhoxbt]

WHOEVER LEAKED THIS JEV KNOT SHOULD BE RUNNING TOPOLOGY

I thought an agent loop meant a while-True with retries and a prayer that it exits.

Apparently not.

This knot's setup is much more interesting:

→ INTAKE parses each packet from one of 3 lanes (email, chat, api) and tags it with an id, no raw text crosses past this node
→ ROUTE sends the packet into one of 3 lobes of the trefoil, picked by jev.route() in a 12 line-per-second trace, no "guess which lobe"
→ DECIDE runs noul and gate at the crossing with a 0.85 floor, PASS or RETRY, no silent escalation to the model
→ ACT writes the draft or queues the call, 609 tokens per draft, every write logged against the packet id
→ LOG closes the loop back to INTAKE for the next packet, flags 7 crossings whose over-under drifted from the trefoil invariant this hour

so you get:

Packets tagged. Lobes routed. Gates floored. Writes logged.

the interesting part isn't making the loop faster.
it's giving every packet a closed route the state can't fall out of.

Instead of a chain that collapses when any step fails, you run 10,082 decisions through a 3-crossing closed trefoil at $0.42, 207ms p50, 54% ACT and 27% IGNORE, and every packet exits where it entered.

Tag the packet. Route the lobe. Close the loop.

[codila @0xCodila]

Jev is the "Internet" moment for the AI industry

It tells your agents and LLMs what to do next, in milliseconds and at almost zero cost

If you set it up correctly, you will have the AI engineer’s stack for 2028

In this article, I show you how https://t.co/x3qn41ejnr

[Rhoxbt @Rhoxbt]

WHOEVER LEAKED THIS JEV TRADING DESK SHOULD BE RUNNING THE BOOK

I thought running an agent on a live orderbook meant throwing GPT at the chart and praying it didn't short the top on vibes.

Apparently not.

This desk's setup is much more interesting:

→ WATCHER streams ticks from 6 assets in parallel, no reasoning yet, pure context shared to the analyst
→ ANALYST calls noul("act?") on every tick in milliseconds, returns p=0.91 or skips, no "maybe wait" allowed
→ CHOICE picks LONG or SHORT from a two-element set with confidence, no "stay in cash while thinking"
→ RISK evaluates side × size against the drawdown budget before the executor touches the book, drops silent breaches at 0.5%
→ GATE compares gate.conf against 0.85, below drops to human, above fills, flags 7 ticks the gate held while the market moved

so you get:

Ticks streamed. Acts gated. Sides typed. Risks capped.

the interesting part isn't predicting the market harder.
it's giving every tick a gate the executor can't fire past.

Instead of streaming 10,115 ticks through a full model and praying the JSON parses before SOL moves, you run them through JEV at $0.42 per 10k, 260ms median, 91.4% gate pass, with every short decision logged against the fill.

Watch the tick. Gate the act. Cap the size.

[codila @0xCodila]

Jev is the "Internet" moment for the AI industry

It tells your agents and LLMs what to do next, in milliseconds and at almost zero cost

If you set it up correctly, you will have the AI engineer’s stack for 2028

In this article, I show you how https://t.co/x3qn41ejnr

[Rhoxbt @Rhoxbt]

we've been using Claude f*cking wrong on agent actions too

your agent reads a YouTube comment, drafts a reply, posts it. no gate in front of the post. one legal threat it waves off as friendly, one hallucinated promise, one spam comment it feeds back as a quote, and 100k subs see the stupid reply before you do.

Jev flips that too.

it's a mode of Jev that cannot write a single word. you give it the event and 3 questions in parallel, it gives back one of three typed answers:

-> a choice from your options -> a score on your scale -> a yes/no
each with a confidence, so your code knows whether to run it or send it to a human.
that's the whole mode.

-> 354 ms per gated decision -> $0.018 per million input tokens -> output is free

-> 10,000 gated decisions for around $0.42

that's not because Jev reads your comments better than Claude.
it's because nobody asked it to write the next thing.
let Claude draft the reply. let Jev decide if it ships. let code gate the action behind the confidence.

the catch: the gate is only as good as the question. "is this reply safe?" is testable, "is this reply good?" is not. keep the heads narrow and the thresholds measurable.

how to wire the 3-head gate in front of every agent action + the exact Choice/Score/Noul schema in the article below

[Argona @Argona0x]

https://t.co/XkfsgqCUug

[Rhoxbt @Rhoxbt]

we've been using Claude f*cking wrong on agent routing too

your agent reaches a decision. it calls Claude, pays for 400 output tokens of "let me think through the options," parses one choice out of a paragraph. 2 seconds of wait. 4 cents per decision. half the time it rationalized itself into the wrong pick.

Jev flips that too.

it's a mode of Jev that cannot write a single word. you give it the state, the possible next steps and the criteria. it gives back the pick, a confidence score and an urgency label. one call, one typed answer.

Choice(instructions="Choose the next step", criteria=...)

that's the whole mode. 256 ms median latency. $0.42 per 10,000 calls. 1,316 live decisions in the clip, every one logged with its score.

that's not because Jev knows your agent's job better than Claude. it's because nobody asked it to write the next thing.

let Claude write the hard parts. let Jev make the typed call. let your code decide what runs next.

the catch: it only works when the next step is one of a fixed set. no free-form "come up with something." if the task needs prose, keep the big model on it. if it needs a routed call with known outcomes, you've been overpaying by 1,000x.

the orb in the clip is my own visual concept, the data is simulated. the engine under it is real: one typed question, one typed answer, every call written to disk in milliseconds.

the full Choice schema and the gate thresholds are in the article below.

bookmark this before your next agent build burns $400 on decisions it could have made for 42 cents.

[kiosa @thegreatest_sv]

https://t.co/KVN1FmmPmy

[Rhoxbt @Rhoxbt]

i genuinely don't understand why no one is doing this with markets yet

Andrej Karpathy, a co-founder of OpenAI, posted a simple idea that hit 18 million views: stop renting a Bloomberg terminal, build an agent that reads the market for you.

you give Grok Bot one folder on its own cloud computer, feed it SEC filings, wires and earnings calls, and it reads them, scores them and files them into a living research desk of every ticker on your watchlist. it compounds like interest, the more you feed it the sharper it gets.

doesn't matter if you're a retail investor, a prop trader or a quant, you need this

the full setup:

create a bot in Grok Bot, name it Desk and add SEC EDGAR and Reuters in Connect apps. it runs on its own cloud computer, so it keeps working while your laptop is shut

paste the prompt from chapter 07 of the article below into its chat. one message builds the folder, the watchlist and the rules

it reads 20 trades you already closed and writes edge.md, 10 patterns of how you make money, each with a real position of yours. no more "i had a feeling" exits

label any filing Desk and every night at 11 PM it saves the original into raw/ and files it on the page of that ticker or sector

at 7:30 AM you get one page with what moved, what it flagged and what waits for your yes, ranked by the 5 tickers you actually hold

correct it once and the fix goes into rules.md, which it rereads before every scan. every Sunday it tells you what you called right and what you ignored

keep it to a few scans a day. one minute-level routine can eat a week of your capital.

five minutes to set up, and you never open 14 tabs before the bell again.

the terminal in the clip is my own visual concept and the market data in it is simulated. same engine, louder feed: one agent ingesting all day on a computer that is not my laptop.

every prompt is in the 7-step course below. or paste the whole article into Desk's chat and it builds the terminal for you.

[Carnage @0xCarnagee]

https://t.co/Sb4TjZpUHH

[Rhoxbt @Rhoxbt]

we've been using Claude f*cking wrong on contracts too

your $200/mo model writes a function AND grades whether it matches the spec. then it writes a second prompt to grade the grading. three turns later you have a boolean back and 40 cents burned.

Jev flips that too.

it's a mode of Jev that cannot write a single word. you give it a response and the contract it was supposed to satisfy, it gives back one of three things:

-> pass or fail
-> which clause broke
-> the exact span that broke it

each with a confidence, so your code knows whether to retry or raise.

that's the whole mode.

-> 40 to 300 milliseconds per check
-> $0.018 per million input tokens
-> output is free

-> 10,000 verifications for around $0.18.

that's not because Jev knows your contract better than Claude.

it's because nobody asked it to write the next thing.

> let Claude write.
> let Jev tell you if the write holds.
> let code decide what to do with the verdict.

the catch: schema only, no free-form rules, and the span index is chars not lines.

how to wire Jev-verify in front of every tool call + the exact contract format in the article below

[Carnage @0xCarnagee]

https://t.co/Sb4TjZpUHH

[Rhoxbt @Rhoxbt]

we've been using Claude f*cking wrong on routing too

your $200/mo model handles every call the same way. a yes/no lookup gets the same full chain of thought as a 10-step reasoning task. you pay for both at the same rate.

Jev flips that too.

it's a mode of Jev that doesn't answer anything. you give it the prompt and the schema, it gives back one of three things:

-> ship this (high confidence, cheap path handles it)
-> escalate (low confidence, send to the big model)
-> hand to code (deterministic branch)

each with a confidence score, so your pipeline knows whether to pay Claude or stop there.

that's the whole mode.

-> 20 to 180 milliseconds per decision
-> $0.012 per million input tokens
-> output is free

-> 10,000 routings for around $0.12.

that's not because Jev is smarter than Claude.

it's because nobody asked it to answer the question.

> let Jev decide who answers.
> let the cheap path win when it can.
> let the big model handle only what matters.

the catch: cascade needs a confidence threshold you tune, and the schema has to match both branches.

how to wire Jev-cascade in front of every LLM call + the exact routing config in the article below

[Carnage @0xCarnagee]

https://t.co/Sb4TjZpUHH

[Rhoxbt @Rhoxbt]

the one skill Claude is bad at is the one skill your agent uses most.

it's ranking. and you're paying for it 300 times a day.

you ask it to pick the top 5 from 50 candidates. it reads them all, writes a paragraph on each, re-reads its own paragraphs to pick. three minutes later the top three change if you re-run it. same input, different winner.

Jev was built for this.

it's a mode that cannot write a single sentence. you hand it a list and a criterion, it gives back one of three things:

-> an ordered list, best to worst
-> the gap between each neighbor
-> a cut-point where the gap goes flat

each with a stability score, so your code knows whether to show 3 or 10.

that's the whole mode.

-> 80 to 500 milliseconds per rank
-> $0.025 per million input tokens
-> output is free

-> 10,000 ranks for around $0.25.

that's not because Jev reads your candidates better than Claude.

it's because nobody asked it to show its work.

> let Claude think about the criterion.
> let Jev order by it.
> let code decide where to cut.

the catch: schema only, 200 candidates max per call, and ties return in input order not alphabetical.

how to wire Jev-rank in front of every retrieval cutoff + the exact criterion format in the article below

[Carnage @0xCarnagee]

https://t.co/Sb4TjZpUHH

[Rhoxbt @Rhoxbt]

we've been using Claude f*cking wrong on contracts too

your $200/mo model writes a function AND grades whether it matches the spec. then it writes a second prompt to grade the grading. three turns later you have a boolean back and 40 cents burned.

Jev flips that too.

it's a mode of Jev that cannot write a single word. you give it a response and the contract it was supposed to satisfy, it gives back one of three things:

-> pass or fail
-> which clause broke
-> the exact span that broke it

each with a confidence, so your code knows whether to retry or raise.

that's the whole mode.

-> 40 to 300 milliseconds per check
-> $0.018 per million input tokens
-> output is free

-> 10,000 verifications for around $0.18.

that's not because Jev knows your contract better than Claude.

it's because nobody asked it to write the next thing.

> let Claude write.
> let Jev tell you if the write holds.
> let code decide what to do with the verdict.

the catch: schema only, no free-form rules, and the span index is chars not lines.

how to wire Jev-verify in front of every tool call + the exact contract format in the article below

[Carnage @0xCarnagee]

https://t.co/Sb4TjZpUHH

[Rhoxbt @Rhoxbt]

SpaceXAI engineer just dropped his prompt for building a team of GrokBot agents on top of Lauren Tan's stack.

this is f*cking insane...

drop this prompt into GrokBot to spin up a team of 30+ agents

then read the full roadmap for building the agent team in the article below.

[Carnage @0xCarnagee]

https://t.co/Sb4TjZpUHH

[Rhoxbt @Rhoxbt]

i quietly closed pytorch after watching this f*cking gold

i work in ml. i asked my agent to visualise what my model actually looks like. it drew a block diagram. then this landed on my feed and i realised i had never actually seen my own network

> 16 inputs, 26 neurons, 25 neurons, one output layer, every connection visible
> relu, sigmoid, every activation hand-set per layer in a GUI someone wrote
> live training signal in the corner, no python, no tensorboard
> every bias, every weight, every edge, one monitor, one window

the thing you call your model is 5 abstraction layers away from a single weight. right now

[Carnage @0xCarnagee]

https://t.co/Sb4TjZpUHH

[Rhoxbt @Rhoxbt]

this is free f*cking gold and my $700/mo ai subscriptions all live on my gpu now

a dev wired his localhost to intercept every subscription call. chatgpt pro, claude max 20x, supergrok heavy. the models all route to his own card.

the boring half of every call never touches the cloud:

> jev routes the easy prompts to the local gpu
> fast-browser-use handles the scraping
> rizzo-flow runs llama.cpp for throughput
> deepseekgui, oriveo, llm2jev stacked for coverage
> claude keeps the hard code, nothing else
> gpu fans hit 3400 rpm
> bill drops from $700 to local power only

the model you were renting was 80% a lookup you already paid for in watts.

your $700/mo "ai stack" is a GPU you already own and a config file you haven't written.

[Carnage @0xCarnagee]

https://t.co/Sb4TjZpUHH

[Rhoxbt @Rhoxbt]

Google DeepMind CEO, Demis Hassabis:

"We're using duct tape right now. Everyone shoves it all in the context window and hopes memory shows up. On hour 41 of a 40-hour agent run, nothing persists

I see founders running 10 agents for 40 hours and I'm not sure the output justifies the input. The AAA vibe-coded game at the top of the charts? Doesn't exist yet"

In this 40-minute YC x DeepMind talk with Garry Tan, Demis names the 3 holes still broken in every agent stack: context, memory, continual learning

Prompt → Context window → Memory → Continual learning → Actually useful agent

Most people are selling you a swarm. The CEO of DeepMind just said the swarm forgets itself every 40 hours

This talk is worth more than most $2,000 AI founder courses

Bookmark and watch it today

Then read the full guide on building agent teams below ↓

[Carnage @0xCarnagee]

https://t.co/Sb4TjZpUHH

[Rhoxbt @Rhoxbt]

SpaceX AI hired this engineer at $1.1M-$1.5M a year because he built the new engineering stack - a 15-bot org that runs itself

In this 83-minute interview, the GrokBot creator shows exactly how to build one from scratch

Chief of Staff → 2 Managers → 13 Workers → Bots pinging first → 99% of work done by text

step 1 → hire one Chief of Staff - every task flows through her, you never DM a worker bot directly

step 2 → add 2 manager bots - they split work, assign, chase, return results up the chain, no copy paste

step 3 → stand up 13 worker bots under them - one role each, each owns a lane, zero overlap

step 4 → make bots write first - they ping you with status, questions, finished work. Not the other way around

step 5 → kill the settings menu - 99% of automations are set up by typing to the bot, no UI, no clicks

SpaceX AI calls this "the new engineering stack"

Most people stop at 1 bot and wonder why nothing works

You don't have to

Bookmark and watch it today

Then read how to hire your first team of Grok Bots in the article below ↓

[yuzu @0xYuzuu]

https://t.co/rnmDv9SzvD

[Rhoxbt @Rhoxbt]

SpaceXAI product team:

"We don't have a product team anymore. We have Cora, Ashley, Pete, Pixel, and Emily - 5 bots that take one question and ship it

Yesterday a user hit a drop in our mobile funnel. Ashley found the leak, Pete wrote the PRD, Pixel mocked two options, Emily cut it into tickets, 5 engineer bots opened PRs. I just picked the mock I liked"

In 37 minutes, they show exactly how one question becomes a shipped feature with zero human handoffs

Data bot → PM bot → Design bot → Eng manager bot → 5 cloud agents → Merged PRs

Most PMs still write every PRD by hand. The 1% already hired a product team that writes, designs and ships it

This talk is worth more than most $1,500 product management courses

Bookmark and watch it today

Then read how to hire your first team of Grok Bots in the article below ↓

[distort @distortgeekin]

Grok Bot could be the first thing you hire instead of prompt

It can own a job, work inside your tools for hours and come back with finished work

In this article, I show you how https://t.co/7KFUugkd5S

[Rhoxbt @Rhoxbt]

SpaceX AI hired this engineer at $900K-$1.3M a year because he built an entire org out of bots - no humans in the loop

In this 30-minute workshop, he shows exactly how to assemble one from scratch

Chief of Staff → UI bot → DevX bot → Infra bot → Head of Operations → Shipped

step 1 → hire Ling Shi Shi as Chief of Staff - she routes every task, you only talk to her, never to the engineers

step 2 → split engineering by surface - Craig owns UI, Steve owns DevX, Hogan owns infra. Same model, 3 memories, 3 contexts

step 3 → add Jenny as Head of Operations - she owns the playbook, every new rule you set propagates to the fleet automatically

step 4 → set a P0 bot that pings every cloud agent every 5 min - no stuck agents, no sleep(300) hacks, no missed deadlines

step 5 → run a nightly audit bot - sweeps for sloppy code, security holes and dead comments, merges clean PRs by morning

SpaceX AI calls this "shipping without engineers"

Most people spend months hiring humans for these roles

You don't have to

Bookmark and watch it today

Then read how to hire your first team of Grok Bots in the article below ↓

[Carnage @0xCarnagee]

https://t.co/Sb4TjZpUHH