ZYNQ

Content Creator | AI Researcher | dm open

[ZYNQ @0xzynq]

My Github https://t.co/XwkQtIg9zu

[ZYNQ @0xzynq]

I SET UP AN AI RESEARCH TEAM THAT WORKS ALL NIGHT AND HANDS ME A BRIEF BEFORE I'VE EVEN HAD COFFEE, AND THE FIRST MORNING IT RAN I ASSUMED SOMETHING BROKE.

It reads every new arXiv paper, every GitHub push, everything relevant on X, and decides on its own what's actually worth keeping instead of dumping the whole feed on my desk like every alert tool I've tried before it.

Whatever survives that first cut gets routed to the right desk, papers, repos, or market, then reranked, checked against everything already sitting in memory so it never repeats a claim it's already filed, and only then does it write the note. It only calls in the expensive frontier model when it's genuinely unsure, which on this run was 42% of the judgments, everything else it just decided on its own and moved on.

I woke up to 144 sources screened, 46 notes written, 19 escalated for a second opinion, and a full brief already sitting in my inbox before 9am, for a total run cost of 25 cents. My co-founder walked past my screen, saw the notes stacking up, and asked when I hired a research assistant.

[Bober_smart @Bober_smart]

https://t.co/7vLHBOOfNw

[ZYNQ @0xzynq]

My Github https://t.co/XwkQtIgHp2

[ZYNQ @0xzynq]

I BUILT A SIGNAL ENGINE THAT TURNS TWELVE MARKETS INTO ONE ROTATING FIELD, AND THE FIRST TIME I SHOWED IT TO A TRADER HE WENT QUIET FOR A FULL MINUTE.

Every asset gets its own thread inside a 7,020-point dome, gold, the S&P, DXY, copper, oil, majors, all scanning, comparing, and connecting routes in real time instead of sitting in separate tabs like normal terminals force you to do.

No grid of tickers, no wall of candles, just the actual structure of how twelve markets talk to each other spinning in front of you while it cycles through scan, compare, connect, rotate, and locks into sync right as the correlations line up on screen. I sent him the raw clip with zero explanation, no context, nothing, and the first thing he asked wasn't what it was, it was how fast he could get read access to it.

That's usually how I know something's actually good, when the questions skip straight past "what is this" and go to "how do I get in."

[Bober_smart @Bober_smart]

https://t.co/7vLHBOOfNw

[ZYNQ @0xzynq]

@Bober_smart yes

[ZYNQ @0xzynq]

I GAVE AN AI FULL CONTROL OF A TRADING ACCOUNT AND TOLD IT NOT TO TALK TO ME UNTIL DAY'S END.

Just picks and fills, no explanations, nothing to approve. It scanned over a hundred tokens in a session, dropped most of them on rug signature or a bad entry window, and only sat down at the ones that passed every check.

Bank opened the day at $12,539 and closed past $20,900, and the one line it left behind was "sold the mistake an hour after the fill." I've been trading manually for two years and I don't leave notes that honest about my own losses.

[Bober_smart @Bober_smart]

https://t.co/7vLHBOOfNw

[ZYNQ @0xzynq]

I RAN MY WHOLE TRADING DESK LIKE A CRIME FAMILY AND IT MADE MORE MONEY THAN MY ACTUAL JOB.

Started the session with $67 and gave every family member one job Arthur scans, Finn compares, John traces, Charlie filters, Curly vetoes, Aberama handles exit depth, Alfie audits, Bonnie retests. Tommy sits at the desk and only moves when the whole family agrees.

No single one of them can pull the trigger alone, that's the entire point. Watched the capital trace crawl from $67 past $400, then $5.5K, then $13.8K, and by the time the session sealed it closed at $19,388.

Sent this to a friend who trades manually and he just stared at the screen for a solid minute before asking who built it.

[ZYNQ @0xzynq]

https://t.co/0ZnWVgd3N6

[ZYNQ @0xzynq]

I forked one of these twenty repos for a client's decision system, and the routing cost came in at $0.00003 per turn, cheap enough that nobody even asked about scaling it.

20 f*cking useful JEV skills, 20 repos, 5 layers, 28.4k stars combined, snapshot September 2026. One judgment model small enough to sit inside a live loop, returning a typed verdict instead of a paragraph nobody wants to parse at inference speed.

Five layers laid out top to bottom: Primitive, 21 repos, typed verdicts not prose, jev-mcp, jev-codex-router, typesafe-mcp, semdecide. Context Gate, 3.4k stars, gates context and done, fast-jev-compaction leading at 3.4k, jev-review, Canny, and winnow sitting at the bottom with zero stars, clearly the newest addition. Surface, 6.9k, browser, desktop, code, graph, jev-ultrafast pulling 5.9k on its own. Tight Loop, 39, decisions inside milliseconds, typesafe-mario, jev-trader, jev-drone, prism-liquidity-agent. Verdict, 18.0k, the answer is the product, json-render carrying nearly all of it at 18.0k stars, sitting next to killmyidea, OneVOneJev, jev-curate.

Every node traces back through connecting lines to one center block labeled JEV, system, one model, and the line underneath explains exactly why that matters: the big model still writes the code, this one only answers yes, no, or which, fast enough to ask every single turn. 2.5Hz cited as a live example, a quadrotor picking its next move mid-flight, camera only.

That's the part that actually closed the deal on my end. Not the star count. $0.00003 per routing decision, about 60% off what a frontier model call would have cost for the same yes-or-no question, run thousands of times a session instead of once.

[ZYNQ @0xzynq]

I forked one of these twenty repos for a client's decision system, and the routing cost came in at $0.00003 per turn, cheap enough that nobody even asked about scaling it.

20 f*cking useful JEV skills, 20 repos, 5 layers, 28.4k stars combined, snapshot September 2026. One judgment model small enough to sit inside a live loop, returning a typed verdict instead of a paragraph nobody wants to parse at inference speed.

Five layers laid out top to bottom: Primitive, 21 repos, typed verdicts not prose, jev-mcp, jev-codex-router, typesafe-mcp, semdecide. Context Gate, 3.4k stars, gates context and done, fast-jev-compaction leading at 3.4k, jev-review, Canny, and winnow sitting at the bottom with zero stars, clearly the newest addition. Surface, 6.9k, browser, desktop, code, graph, jev-ultrafast pulling 5.9k on its own. Tight Loop, 39, decisions inside milliseconds, typesafe-mario, jev-trader, jev-drone, prism-liquidity-agent. Verdict, 18.0k, the answer is the product, json-render carrying nearly all of it at 18.0k stars, sitting next to killmyidea, OneVOneJev, jev-curate.

Every node traces back through connecting lines to one center block labeled JEV, system, one model, and the line underneath explains exactly why that matters: the big model still writes the code, this one only answers yes, no, or which, fast enough to ask every single turn. 2.5Hz cited as a live example, a quadrotor picking its next move mid-flight, camera only.

That's the part that actually closed the deal on my end. Not the star count. $0.00003 per routing decision, about 60% off what a frontier model call would have cost for the same yes-or-no question, run thousands of times a session instead of once.

[ZYNQ @0xzynq]

I let a cartoon bee named Trader Fly run this position, and the panic label popping up over its head is exactly what happened to my own hands watching it.

HODL down 3.94%, MOON down 5.49%, FOMO down 6.89%, dips flashing across the ticker tape overhead. A little blue-bodied bee sat hunched at a tiny desk between two bigger ant-like figures, a glowing green screen stacked with candle charts lighting up the scene, a speech bubble tagging it plainly, Trader Fly went long 3x and is watching the price, followed by a second bubble a beat later just saying "panicking."

Underneath the scene, the actual numbers backed the joke up. Position PNL +81.03%, long 30x, entry 48.20. 43,468 trades logged across 4 venues in the last 50ms alone. Account balance ticking up +$218 a second in real time while the FLY/USDT candles chopped sideways on 0.35 second intervals. Sentiment gauge at the bottom sat at 59, greed.

That's what actually made it funny instead of just cute. The cartoon panic wasn't decoration sitting on top of a static chart. It was reacting to a real, ticking PNL number that could just as easily have flipped the other direction the moment the candles turned.

[ZYNQ @0xzynq]

I let a cartoon bee named Trader Fly run this position, and the panic label popping up over its head is exactly what happened to my own hands watching it.

HODL down 3.94%, MOON down 5.49%, FOMO down 6.89%, dips flashing across the ticker tape overhead. A little blue-bodied bee sat hunched at a tiny desk between two bigger ant-like figures, a glowing green screen stacked with candle charts lighting up the scene, a speech bubble tagging it plainly, Trader Fly went long 3x and is watching the price, followed by a second bubble a beat later just saying "panicking."

Underneath the scene, the actual numbers backed the joke up. Position PNL +81.03%, long 30x, entry 48.20. 43,468 trades logged across 4 venues in the last 50ms alone. Account balance ticking up +$218 a second in real time while the FLY/USDT candles chopped sideways on 0.35 second intervals. Sentiment gauge at the bottom sat at 59, greed.

That's what actually made it funny instead of just cute. The cartoon panic wasn't decoration sitting on top of a static chart. It was reacting to a real, ticking PNL number that could just as easily have flipped the other direction the moment the candles turned.

[ZYNQ @0xzynq]

https://t.co/0ZnWVgd3N6

[ZYNQ @0xzynq]

I ran this bot live for a week straight, and the realized number sitting at $946,207 is what finally got my business partner to stop asking when I'd "get a real job."

0x6e1d-0fa, live mainnet, round #7165. Realized P&L $946,207 since April 1st, 4,082 trades, 71% win rate, Sharpe 4.21. Best hit tracked separately, +$123,197 at x40.9, APR against BTC 490K, yes at 1.2x, entry $3,088, payout $126,285.

Pattern engine underneath ran its own signal chain live, CHoCH, BOS, FVG, feeding into a signal node that fired "execute short x3.09," entry 18.2, target 74,850, predicted reward if target hit +$4,629, order already sizing at Kelly 0.10, staging live rather than sitting as a backtest number.

Network graph mapped it out visually, bear cluster, catalyst ring, hub prime, 62 nodes, 148 edges, confidence sitting at 94.6%, P(up) 0.71 vs P(down) 0.29.

BTC 5min pulse ran underneath the whole thing live, current price $75,090.53 against a $75,000 line to beat, order book stacking both directions in real time.

That's the number that actually ended the argument. Not the win rate. $946,207 realized, sitting there in green, updating while we were still talking about it.

[ZYNQ @0xzynq]

https://t.co/0ZnWVgd3N6

[ZYNQ @0xzynq]

@sir4K_zen Yes

[ZYNQ @0xzynq]

https://t.co/0ZnWVgd3N6

[ZYNQ @0xzynq]

I ran a routing decision through JEV for a client pitch, and the cost readout landed at $9.32 saved before I even finished explaining what the dashboard was showing.

214 models lit, 26 currently warming, 268 decisions logged, cost per decision sitting at $0.0001, latency 0.2ms. The rule sat printed plainly in its own box: JEV picks a model, Picsart makes the asset, JEV gates the re-run, and the human says whether it's actually good.

One prompt fired into the prompt bar and radiated straight out through the router as hundreds of thin lines, ima_28 landing as the routed image model, picked by JEV at 3.45 credits, snapshot logged and stamped as "routed" the instant it resolved. Feed underneath kept scrolling live, route, gate, judge, score, each decision tagged with its own credit cost, none of them silent.

The number that actually closed the deal was in the comparison table at the bottom: picking a model manually used to run 2.1 seconds, scoring against a brief 1.8 seconds, gating a re-run 2.4 seconds, JEV collapsed all three to 0.2ms each. Cost per call dropped from $0.030 to $0.0001. Full re-run avoided far more often than a re-run got triggered.

That's the part I didn't need to explain twice. 214 models, one prompt bar, and a dollar figure sitting in the corner proving the routing decision paid for itself before the client asked a single follow-up question.

[ZYNQ @0xzynq]

I ran a routing decision through JEV for a client pitch, and the cost readout landed at $9.32 saved before I even finished explaining what the dashboard was showing.

214 models lit, 26 currently warming, 268 decisions logged, cost per decision sitting at $0.0001, latency 0.2ms. The rule sat printed plainly in its own box: JEV picks a model, Picsart makes the asset, JEV gates the re-run, and the human says whether it's actually good.

One prompt fired into the prompt bar and radiated straight out through the router as hundreds of thin lines, ima_28 landing as the routed image model, picked by JEV at 3.45 credits, snapshot logged and stamped as "routed" the instant it resolved. Feed underneath kept scrolling live, route, gate, judge, score, each decision tagged with its own credit cost, none of them silent.

The number that actually closed the deal was in the comparison table at the bottom: picking a model manually used to run 2.1 seconds, scoring against a brief 1.8 seconds, gating a re-run 2.4 seconds, JEV collapsed all three to 0.2ms each. Cost per call dropped from $0.030 to $0.0001. Full re-run avoided far more often than a re-run got triggered.

That's the part I didn't need to explain twice. 214 models, one prompt bar, and a dollar figure sitting in the corner proving the routing decision paid for itself before the client asked a single follow-up question.

[ZYNQ @0xzynq]

https://t.co/0ZnWVgd3N6

[ZYNQ @0xzynq]

I killed 1,164 losing configs to keep the 18 that made it through, and the survivor sitting live right now is holding at $83,997.

Self-improving trading machine, Horizon, Grid-DCA desk, BTC-PERP, non-custodial. Six-stage pipeline running as a closed loop, research, code, backtest, live, post-mortem, fine-tune, currently sitting at stage 6 of 6, 100% through this cycle. Research generated 17 ideas, code turned those into 8 variants, backtest ran 1,209 times, live trading narrowed it to 6 configs, post-mortem read 44 fails, fine-tune landed a +2.2% edge. 2,246 configs killed in the loop, 18 deployed as survivors.

The strategy mesh visualized every surviving config as a node, four regime types color-coded, trend regime, mean revert, vol breakout, funding edge, edges drawn only where two survivors actually agreed on a shared regime. One node per config, one edge per shared conviction, not a decorative graph.

Kelly criterion panel underneath recommended 36% optimal sizing off a 60% win rate and 1.68 payoff ratio, full Kelly math shown, not just the output number. Checkpoint tape logged 524 durable states, resume-where-it-broke instead of re-running from scratch every cycle.

Live grid engine held the survivor config, $83,997 current, average entry $83,780, L2 at $83,850, order grid pre-committed with fills already stacking, $220, $297, $401, $541, $731, all filled climbing toward the $85,050 take-profit.

That's the actual case for the system. Not one lucky trade. 1,164 dead configs behind the one still standing, and a live position built entirely from what survived.

[ZYNQ @0xzynq]

I set up a six-legged bug to learn how to ride a bike for a kid's animation test, and the whole crew stopped scrolling their phones to watch it wobble on the first push.

BLAZT fly, animated short, a little purple-bodied creature perched on a red bicycle in an empty gray training yard, balance gauge pinned dead center on the right labeled "keep it upright," falls counter reading zero. A row of traffic cones scattered across the grid marked out the practice course, nothing fancy, just enough obstacles to make the ride feel like it was going somewhere.

Ride log underneath tracked the actual attempt in real time, 2.0 seconds in, first push logged, 0.0, feet on the pedals, the lesson bar filling in slowly as the little guy found its balance. Small push, big wobble, exactly the caption running under it, and that's genuinely what sold the room, not a polished cinematic ride, six spindly legs barely keeping a bicycle upright on the very first attempt.

That's the moment everyone leaned in. Not the concept of a bug on a bike. Watching zero falls hold through an actual wobble, live, with a real physics-feeling stumble instead of a scripted animation cycle.

[ZYNQ @0xzynq]

I set up a six-legged bug to learn how to ride a bike for a kid's animation test, and the whole crew stopped scrolling their phones to watch it wobble on the first push.

BLAZT fly, animated short, a little purple-bodied creature perched on a red bicycle in an empty gray training yard, balance gauge pinned dead center on the right labeled "keep it upright," falls counter reading zero. A row of traffic cones scattered across the grid marked out the practice course, nothing fancy, just enough obstacles to make the ride feel like it was going somewhere.

Ride log underneath tracked the actual attempt in real time, 2.0 seconds in, first push logged, 0.0, feet on the pedals, the lesson bar filling in slowly as the little guy found its balance. Small push, big wobble, exactly the caption running under it, and that's genuinely what sold the room, not a polished cinematic ride, six spindly legs barely keeping a bicycle upright on the very first attempt.

That's the moment everyone leaned in. Not the concept of a bug on a bike. Watching zero falls hold through an actual wobble, live, with a real physics-feeling stumble instead of a scripted animation cycle.

[ZYNQ @0xzynq]

I set a pigeon loose on my own trading terminal for a demo, and it found the keyboard before I finished explaining the setup.

BLAZT Terminal, SOL/demo market, paper mode, no real funds on the line. A clean sine wave rolled across the chart in the background while a low-poly pigeon wandered the desk between a desk lamp, a notebook, and a mug, keyboard control panel tracking its state on the right: exploring, find the keyboard, behavior log timestamped at 0.0 with "desk detected" as the first entry.

That's the whole bit, and it's the point. A pigeon. A keyboard. One simple task. No dashboard full of fake P&L, no inflated win rate, just an agent stated plainly as paper mode, watching a bird figure out where the input device even is before anyone pretends it's making real trading decisions.

I showed it to a few people expecting a laugh about the pigeon. What actually landed was the honesty of the label sitting bottom right the whole time, paper mode, no real funds, while every other demo in the space quietly skips that disclaimer.