CHROMA IS NOW ON GITHUB. YOU CAN CLONE IT, RUN THE SCANNER AND INSPECT HOW IT WORKS. I’ve published CHROMA v0.2: an open-source Solana research tool with live market scanning, eight explainable checks, a ranked target queue and local event history. https://t.co/2tmx1901C4 Three modes are included: → DEMO - an offline walkthrough with synthetic targets → SCAN - live DEX Screener data, candidate ranking and repeated observations → TRADE - Jupiter quotes, transaction simulation and explicitly confirmed swap execution The eight checks cover price availability, liquidity, volume, activity, buy pressure, price stability, pair age and turnover. The thresholds are visible and configurable. A score is a research filter, not a guarantee that a token is safe or profitable. Each saved observation keeps the measurements, thresholds and status changes together. You can inspect the history, add notes and export records as JSON or Markdown. Scanning requires Node.js 22+, with no wallet, API keys or package installation. Trading has separate dependencies and wallet setup. Scanning never places an order automatically; execution requires a separate command and per-order confirmation. The repository includes setup instructions, methodology, configuration, tests and security notes. MIT licensed. Try the workflow, inspect the rules and open an issue when something needs fixing. The implementation is public now, including the parts worth questioning.

CHROMA'S ON-SCREEN LEDGER CLOSES AT +28.64 SOL. 13 POSITIONS SETTLED. 5 CANDIDATES FILTERED. I created CHROMA to connect token discovery, eight parallel checks and a decision log that follows each candidate through entry, tracking and exit. The closing figures shown in the video: → Session ledger: +28.64 SOL → Last closed position, OPAL: +5.22 SOL → Signals parsed: 2,520 → Positions settled: 13 → Candidates filtered: 5 The workflow keeps selected and rejected candidates in the same history. Each outcome should connect back to the checks and decisions that preceded it. These are figures displayed in the animation, not verified trading income. Establishing realized net profit requires an execution record that includes costs, fees and losses. The aim is to make the number explainable: which positions contributed, what was rejected and what remained after execution.

LEOPARD SCAN: 277.78 SOL → 310 SOL. +32.22 SOL. AN 11.6% INCREASE ON THE DISPLAYED BALANCE. I created Leopard Scan to track opportunities, prioritize candidates and make the history behind each target traceable. The session shown in the video records: → Starting balance: 277.78 SOL → Ending balance: 310.00 SOL → Displayed session yield: +32.22 SOL → Balance increase: +11.6% → Captures recorded: 5 The workflow has four core jobs: → SCAN - identify candidates worth following → TRACK - maintain their status as conditions change → PRIORITIZE - organize the target queue → RECORD - preserve the sequence of events and outcomes The five-target view brings estimated values, tracking status and activity history together. The aim is to show why something became a target and what happened afterward, rather than leave a balance chart as the only record. These are on-screen figures, not a verified trading record. Realized net profit needs to account for execution costs, fees, losses and any deposits or withdrawals. The GitHub repository and working scanner are coming soon. The release will move the project toward public testing, where the implementation can be inspected and its results evaluated directly. A scanner should earn its place through repeatable results, not a convincing screenshot.

the easiest way to ruin a digital character is to move the camera closer. at thumbnail size, a smile and a soft silhouette do most of the work. zoom in and every shortcut becomes visible: flat fur, identical strands, a scarf that looks glued to the body. this dot study puts those details under a microscope. → look - inspect the shape and surface → groom - establish the direction of the fibers → focus - bring individual strands into view → flow - introduce movement through the coat → check - inspect the silhouette and boundaries → keep - preserve the state for the next pass the useful detail is how much happens without changing the expression. the coat catches light, loose fibers break up the outline, and the pink scarf gives the softness a different texture to sit against. the interface labels the coat at 168k fibers. its telemetry is illustrative, so those counters aren't a performance benchmark. the close-ups are what the viewer can actually judge. more detail isn't automatically more convincing. if everything bends together, catches the same light and moves at the same speed, it still looks like one plastic object. a character starts feeling alive when its smallest parts stop looking identical.

Sam Altman watching creators turn prompts into paid drops with Fanvue + Higgsfield while you’re on your 7th ChatGPT revision of a two-line email

$15,728 → $23,831 IN ONE SESSION. THE WALLET YOU'VE BEEN COPYING MIGHT BE A BOT. I BUILT A $0 TRACKER THAT TELLS YOU, STRAIGHT FROM THE CHAIN, IF IT'S SAFE TO FOLLOW. it's called WolfBrain. my desk only follows the old, quiet wallets that pass every check. the clip is the ledger on that session. +$8,153, +52% big buy → lands in a block → shows up on my screen → i see the wallet behind it paste any wallet and it tells you: sniper, flipper, accumulator, whale or bot. the calls come from TypeSafe Jev. code pulls the facts, Jev decides and says how sure it is. no wallet connect, no keys, no sign-up. paste the wallet you've been copying. i'm curious what it calls yours.

the guy who helped build chatgpt just raised $40M for a model that can't write a single sentence diogo almeida was at openai for chatgpt and instructgpt. two years in stealth. on sept 15 he came out with jev chatgpt taught models to talk. jev is physically incapable of prose. it does one thing: decides. yes or no. which option. how urgent 70-500ms per decision. $0.042 per million tokens now open your agent. every time it asks "is this source relevant?" or "which worker next?" it pays a frontier model to think out loud about a yes/no that's the leak one dev classified 500 emails for 3.5 cents. total. not per email another plugged it into a live monad order book. a real order every 300 milliseconds in their own test jev matched frontier-model consensus at 67.8% for $0.0004 a case. gpt-5.6 terra got 67.9% for $0.0304 same accuracy. about 76x cheaper the model that writes and the model that decides don't have to be the same model

MY TERMINAL TOOK $5,007 TO $8,625 IN A COUPLE OF HOURS. 25 marks came in. I took 12 and let 13 go. the ones I let go were the loud ones. the ones I took had been asleep for a year, one of them dormant for 363 days, and they started moving before any feed said a word. clips stayed small on purpose. price impact under two percent, so nothing I did showed up on the book before the fill. 83 percent of the fills came back green. the misses were cheap because size was never the point. the edge is not seeing more. it is watching wallets nobody has looked at since last year.

THE LOOP WAS ALWAYS THE ANSWER. NOBODY TOLD YOU IT COULD BANKRUPT YOU. I've said it since day one: you stop prompting, you start running loops. Agents that dispatch, act, verify, remember, hand off — and never wait for you. The part I couldn't answer until now: a loop that runs for 8 hours dies of a quadratic token bill by hour two. Every step re-reads every step before it. The transcript is the killer. @Marko_Poly just wrote the fix down. Same task, same model: 1,062,387 tokens with the full history, 65,408 with a state file. 16x cheaper - and it scored higher. You don't win the loop era with a bigger model. You win it by paying for the state, not the transcript. Which one is your loop paying for?

166,700 fruit fly brain cells just flew an FPV drone into a wall. Same simulated brain that typed FizzBuzz. This time it got a controller. it Called FPV PILOT. NeuroMechFly poses the fly on a DJI-style remote. Visual neurons read the drone feed - left eye at 177 Hz, right eye at 129 Hz. Steering neurons push the sticks. Forward speed 1.49 m/s through a bombed-out desert town. Every turn decoded straight from spiking cells a 6,588 firing at cruise. 8.8 seconds in: a wall. At impact the brain lights up. 15,628 cells active, more than double cruise. Then the feed cuts to black. Signal lost. Forward: 0.00 m/s. Turn: 0.00 rad/s. 100M years of evolution dodging flyswatters. 9 seconds against a brick wall.