Mojunel.eth

YOU’RE OVERPAYING FOR AI BY 1,000X. I found a tool called XERJ Reader, and it doesn’t generate a single word. Here’s how it works: 1. Drop in your data: PDF, Word, email, JSON, CSV, logs. No schema, no setup 2. Ask a question in plain words 3. Reader scans the whole document in a single pass 4. You get the exact passage highlighted in the source, with its position 5. Not in the doc? It tells you “no answer” instead of making something up It can’t make up a word it never writes. The only answer it can’t give you is a wrong one. And the math nobody wants to run is f**king brutal: → $0.01 per 1M tokens. Output is free → 1,050x cheaper than Opus 5.5 → 26x cheaper than Haiku 5.5 → Roughly 4x cheaper than Jev Same question. Same file. Three models. Opus writes you a paragraph. Haiku writes you a shorter paragraph. XERJ writes nothing. It just underlines the sentence that was already there. 10 questions on a 100 KB doc: Reader → under half a second LLM agent → about a minute The problem was never the model. You’ve been paying for an essay when all you needed was a highlighter. No hallucinations. No waiting. No huge bills. Just the answer, highlighted. Waitlist opens today. Bookmark this and be one of the first to start using it ↓

One agent scoring its own find is a guaranteed yes. Five that never see each other's work built this $100 desk. I wired one prompt on Solana. Five agents, each doing one job. A coin moves left to right and any agent kills it. 1. Finder scans every new pair. Under $20k liquidity or past 6 hours: dead. 2. Auditor reads contract and holders. Top 10 above 35% or dev above 5%: dead. 3. Judge scores 0 to 100. Under 80: dead. 4. Stager sizes at 4% of the book. Sets the -25% stop before entry. 5. Closer sells half at double, quarter at triple, trails 30% under the high. Seven rules sit above every score. 6 open positions, 3 entries an hour, -10% day kills the desk until midnight. The Finder never knows what the Judge scored. The Auditor reads contracts, not scores. Something goes wrong and I get a name, not a story. Rule 7 saved the desk. The creator pushed tokens toward the exchange and the Closer cleared at -6% in one block. The coin lost 91%.

HE PICKED UP A LEVITATING POOL OF WATER FROM A DIRT ROAD AND OFFERED IT TO A GOAT 900k views. 63k likes. 1250 comments. 12 seconds. fisheye POV walking down a dusty dirt road, a floating disk of water suspended in the air. he reaches out, plunges his hand through the liquid surface, scoops up water, and holds it out. a curious goat walks right up, dips its head into the floating pool, and drinks why it works: → the camera angle mimics a natural first-person walk: low, fisheye lens, shifting perspective, and a dusty boot stepping at the bottom of the frame → surreal physics made to look mundane. the way the water ripples, distorts the sun, and splashes feels completely tangible against the dry, barren landscape → flat midday lighting with no artificial fill. the harsh sun catching the edges of the floating water disc makes the impossible look like raw phone footage → the sequence unfolds naturally: discovering the anomaly, interacting with the liquid, and sharing the moment with the animal. the absurdity arrives effortlessly, never forced → one hand, one road, one surreal mechanic. almost nothing to keep consistent → picsart.go.link/31F4Z the takeaway: realism comes from what you leave in. tactile physical interactions and natural camera imperfections do more than any high-end render setting single-event surreal shots like this are what Seedance 2.5 does best, and you can run it inside

Most dangerous filter a memecoin bot can carry. I wired a seven-gate checklist and fed it 50 launches. 41 orders died before signing. 1. Authority. Both mint and freeze read null, or the order never fires. 2. Sell simulation. Dry-runs the full exit before a dollar moves. Revert means honeypot. 3. Liquidity. LP burned or locked 30+ days, order capped at 2% of pool. 4. Holder concentration. Top-10 wallets over 30% after stripping pool and burn kills it. 5. Creator history. Two rugs from the deployer in ten launches, coin rejected. 6. Price impact. Chase over 15% or impact above 3% at the moment of signing. 7. Freshness. Any gate older than 10 seconds stops the entire chain. One fail kills the trade. No overrides. 50 in → 41 refused → 9 through. Score-only wallet: down 34%. Gate wallet: up 9%. The 6x runner got blocked at one gate. That is what 41 refusals cost.

I TOLD GROK BOT: “Create the most insane viral AI vehicle stunt video that could make me my first $500.” I expected something stupid. I definitely didn’t expect this. It gave me a scene where a futuristic supercar literally launches off a ramp on the Golden Gate Bridge, plunges straight into the water, activates vertical jet thrusters, flies back up, and lands smoothly on the road. It looks weird and interesting enough to make you stop scrolling. And that’s basically the whole strategy: > make the first frame impossible to ignore > use one idea people understand instantly > keep the environment realistic > make something progressively weirder happen > end before people get bored The funny part is that you don’t need some huge cinematic story. You need one visual idea that makes someone think “WTF am I watching?” and keeps them there for another 5 seconds. Grok handled the concept. AI handled the scene ↓ picsart.go.link/31F4Z I just turned the insanity into a video people would actually stop for. One weird prompt. One 45-second clip. Now I want to see how far I can push it toward that first $500.

Nuts, token-cost team in 10 repos. Claude Code Max at $100 a month and agents still torch the plan limit on raw tool noise. compression. filtering. replies. costs. retrieval. 01 LLMLingua → squeezes prompts up to 20x before the API call github.com/microsoft/LLMLing… 02 rtk → filters git, test and build output, cuts 60–90% of dev-command tokens github.com/rtk-ai/rtk 03 headroom → compresses logs, JSON and RAG chunks before the model reads them github.com/headroomlabs-ai/h… 04 lean-ctx → reads signatures or diffs instead of dumping whole files github.com/yvgude/lean-ctx 05 caveman → forces terse agent replies, cut 33.2% of session tokens across 54 runs github.com/JuliusBrussee/cav… 06 TokenEconomizer → Microsoft cost controller for Claude Code, drops mean cost 18.4–30.9% github.com/microsoft/TokenEc… 07 code-review-graph → code graph for the agent, 2,196 tokens per answer vs 143,594 github.com/tirth8205/code-re… 08 graphify → turns repo and docs into a queryable graph, zero LLM credits github.com/safishamsi/graphi… 09 token-savior → navigates code by structure and carries memory across sessions github.com/Mibayy/token-savi… 10 jcodemunch-mcp → fetches the one function you need, 94.5% fewer tokens per lookup github.com/jgravelle/jcodemu… compress → filter → shorten → cap → retrieve rtk engine runs BUSL-1.1 and jcodemunch charges for commercial use. Fewer tokens per task ↓

Karpathy's repos just ate my entire AI path 7 repos. 306,741 stars combined. I went through every single one in order. micrograd came first. About 100 lines of Python for backprop from scratch. nn-zero-to-hero. 8 free lectures from a single neuron all the way to GPT. minbpe. Tokenization in minimal code. 10.9k stars. I nearly walked past it. Every model I touched after minbpe ran better than the day before. nanoGPT: 64.3k stars but deprecated. Its own README sends you to nanochat. nanochat: 59.1k stars. About $48 for what cost about $43,000 in 2019. autoresearch closed the whole chain. 98.9k stars. Agents on 5-minute loops. About 100 experiments ran overnight while I slept. Kept or dropped by loss. Each repo stands on the one before it. Skip one step and it all goes dark. Start with one file