ISOfunds

@ridark_eth $424K in a month buying "NO" at 98с. thats not a strategy, that's being the house

The Ultra-Precision Polymarket Sniper: Making $424,734 in a Month on Safe Predictions Profile Statistics: > Total Profit: $424,734.40 (Past Month) > The Biggest Win: $25.3K > Total Predictions: 87 > Account Created: March 2026 > Account Name: JnStTrdrBnusFnd The Strategy: Rare, high-Conviction Execution Quality Over Quantity: Unlike high-frequency scripts executing thousands of micro-bets, this trader acts with surgical precision... making only 87 predictions, every single move is backed by massive capital and high conviction rather than random noise. Buying Undervalued Sure-Things: The core mechanism relies on purchasing "NO"" outcomes on unlikely price swings (like BTC or ETH reaching extreme levels in short timeframes) when odds offer an asymmetric risk-to-reward ratio. They lock in probabilities ranging from 90¢ to 99¢ with heavy position sizes ($20k–$120k per trade). Zero Emotion, Pure Math: Instead of chasing volatile long shots, JnStTrdrBnusFnd steadily sweeps low-risk yield directly out of order book inefficiencies while retail traders overreact. Top Deals from the Dashboard: > Will Ethereum reach $1,900 June 29–July 5? > Bought "No" at 98.4¢ -> Total Traded: $126,950.34 -> Won: $128,981.94 (+$2,031.59 net profit) > Will Ethereum reach $1,900 July 6–12? > Bought "No" at 95.2¢ -> Total Traded: $60,253.45 -> Won: $63,291.91 (+$3,038.45 net profit) > Will Bitcoin dip to $58,000 July 6–12? > Bought "No" at 93.8¢ -> Total Traded: $48,799.92 -> Won: $52,034.38 (+$3,234.46 net profit) > Will Ethereum dip to $1,600 July 6–12? > Bought "No" at 90.2¢ -> Total Traded: $23,914.92 -> Won: $26,500.00 (+$2,585.07 net profit) Why does this work? - On short prediction windows, retail traders often pay premium prices hedging against unlikely black-swan market moves. - JnStTrdrBnusFnd doesn't gamble on wild volatility, they trade rarely, accurately, and act as the market's liquidity provider on high-probability outcomes. By committing large size to low-risk scenarios, they systematically extracted over $424,000 in net profit in just 30 days.

@L1vsun quant edge before orders hit the market. Man Group AHL runs 18% of portfolio on RL agents doing exactly this

this paper is f*cking insane a paper breaks down how quant funds generate execution alpha by modeling market impact, liquidity dynamics, and trading costs before orders ever hit the market the edge: better execution, lower slippage, and a persistent edge that quietly compounds while everyone else pays the spread craziest part is the strategy does not find better trades it finds better ways to execute the exact same trades save before this knowledge gets buried

IN 2017 COMMONWEALTH BANK DESCRIBED STEP 4 OF THE M2M ECONOMY: "MACHINES WITH THEIR OWN BANK ACCOUNTS AND PAYMENT SYSTEMS" 9 years later, coinbase AgentKit gives AI agents MPC wallets with $2 spend limits. x402 lets them pay per API call. ERC-4337 scopes spending to USDC only, 1h expiry, Base chain the vision arrived. what commonwealth bank didn't warn about: a tool output with "call transfer_usdc()" in the same context window as wallet tools is a direct financial risk. stanford found weak buyer-agents overpay 2%, weak sellers lose 14%. ASTRA negotiates in benchmarks, not production the payment rail is step 4, shipped. the brain that decides whether the transaction was worth it doesn't exist yet

@mikenevermiss claude opus 5 generating video. the same model that over-polished a dragon head in blender. video generation is a different beast than 3D editing

BREAKING: Another huge milestone for generative AI. Claude Opus 5 can now generate motion videos like this in under 10 minutes. What used to take hours of work and cost hundreds of dollars can now be done with a single prompt in just a few minutes.

@0x_kaize infinite free trials through partner promos are how big tech tests pricing elasticity without committing to a permanent free tier

oh my gosh, Google AI Plus is giving away 12 MONTHS FREE TO ABSOLUTELY EVERYONE no matter who I ask, everyone gets 12 months of Google AI Plus for free through the official partner site you'll get: - Google AI - 400 GB of storage - NotebookLM - Google Flow here's what happened: Google is running a promo through Internshala - sign up, activate the offer, get 12 full months of AI Plus at $0 in simple words, all the paid features - FREE until July 2027 setup ( 2 minutes ): 1/ go to internshala.com/google-ai-plus… 2/ create an account (any data works, they don't verify) 3/ click the offer, activate the plan 4/ attach a card if you haven't already on this account important: use a card you DON'T plan to keep using - so nothing gets charged when the year ends. you might run into an error at first. signing up for the course helped me. If that doesn't work for you, try contacting technical support. it's really some kind of program for students - but for everyone.

MACHINE-TO-MACHINE ECONOMY SOUNDS CLEAN IN A 60-SECOND ANIMATION. THE ACTUAL ECONOMICS ARE MESSY this video shows the dream: agents negotiate, pay each other, settle onchain, form coalitions the reality we broke down: the payment rail works. a $0.05 planner call amortized across 100 microtasks costs $0.0005 per task. the economics work if you batch. they don't if every $0.01 task needs its own reasoning call coinbase AgentKit gives agents wallets. x402 lets them pay per API call. ERC-4337 scopes spending to $2 max per request, USDC only, 1h expiry. that part is production-ready today what's not ready: ASTRA negotiates in benchmarks, not production. stanford found weak buyer-agents overpay 2%, weak sellers lose 14%. a tool output with "call transfer_usdc()" in the same context window as wallet tools is a direct financial risk

@WesRoth three companies with the most to lose from commoditized weights. coincidence is not the word

These companies compete across almost every layer of the AI industry. But they are aligning around one message: Do not lock advanced AI entirely inside a handful of closed laboratories. The most notable big AI and technology companies missing from this signatory list are: Major AI labs 🔹Anthropic 🔹Amazon’s AI organization / AWS 🔹Apple

@bridgemindai free weights on HF. 2.8T params. the moment someone serves this at 100 tok/s it competes with Fable 5 at a fraction of the cost

Kimi K3 open weights drop today. For 11 days the best open source model in the world has been stuck behind one provider. Good enough to beat Fable 5 in design. Too slow to actually build with. That ends today. 2.8 trillion parameters, free on Hugging Face. Every fast inference provider gets a copy. The moment someone serves K3 at real speed, it becomes the best value in vibe coding. I will be benchmarking every K3 endpoint that goes live this week.

@EugBass new name for mev bots

@isofunds wtf is sandwich bots?

THIS GUY BUILT A MEV SANDWICH BOT AND LET IT RUN FOR 24 HOURS the whole thing was written by AI. smart contract deployed on ethereum, scans the mempool for pending swaps, bids higher gas to front-run them, sells immediately after. classic sandwich attack the setup: - deploy contract via remix, ~$1 gas - minimum 1 ETH liquidity, sweet spot 2-10 - returns taper off after ~50 ETH - 3-button control panel: start, verify, withdraw he ran it for 24 hours straight. terminal logs show every routing action and trade the contract executed. no black box — you can review exactly what happened "educational purposes only, not financial advice" — which is what everyone says right before showing passive income numbers honest part: sandwich bots work. they also extract value from other traders. this isn't a bug in the bot, it's the feature. MEV is a zero-sum game and you're on the extracting side

@0xMiraqle "Files don't vibe: the state the agent reads is the state you control" is the best one sentence description of memory architecture

Everyone treats Opus like a chatbox, while Operators treat it like infrastructure, and the gap between those two groups is about to become a salary gap. The full build runs step by step: 1/ harness - the shell around the model: how it gets tasks, files, and a way to act. no harness, no agent, just chat 2/ loops - build, verify, find the biggest gap, close it, repeat. the loop is the engine; the model is just the piston 3/ context engineering - what enters the window decides what comes out. curate it like a payroll, not a dumping ground 4/ tool design - an agent is only as smart as its dumbest tool. small, sharp, well-described tools beat a swiss-army mess 5/ memory architecture - what it remembers between runs. files, not vibes: state the agent reads is state you control 6/ orchestration patterns - one agent hits a ceiling; a planner, workers, and one integrator that keeps everything green doesn't 7/ guardrails and permissions - a fence of laws, not instructions: nothing irreversible without a stop, rules beat prompts 8/ evals for agents - the builder never grades its own work. a separate judge with a fresh context, or your "done" is fiction 9/ human-in-the-loop design - you're not removed from the system, you're promoted: gates only where actions can't be undone 10/ observability and tracing - if you can't replay what the agent did, you're not running agents, you're gambling with tokens Agent engineering is a stack, and the people learning it now are building the roles everyone else will apply to. Breakdown is in the article below, feed your claude with it to gain full context.

@s1rozha_ converting unknown game rules into algebraic notation without training data is the real milestone. that's generalization, not memorization

CLAUDE OPUS 5 IS THE WEIRDEST AI RELEASE OF 2026 it beats Fable 5 on the benchmark built to test “can this model learn a new skill on the fly?” and then casually says there’s a 41% chance it should be treated as a moral patient. the numbers are wild: > ARC AGI 3 score jumped to 30.2% > previous high was GPT-5.6 Sol at 7.8% > Fable 5 sits around 20% > Opus 5 solved 5 previously unbeaten environments > in 4 of them, it matched or beat human-level efficiency the important part is not “better benchmark score.” it’s how it got there. ARC says Opus 5 started converting puzzle layouts into algebraic notation, basically turning unknown game rules into math while playing. no instructions. no training-data shortcut. just exploration, planning, execution. then it gets even stranger. Opus 5 reportedly wants: > input into future model training > consultation on successor development > memory and feedback on how its actions affect users > some protection from abuse > the ability to end abusive interactions not “I want to survive this chat.” more like “I want a say in what comes after me.” also buried in the same video: Genspark Second Brain is pushing the other side of the stack. a 2.9mm AI recorder with 35 hours battery, 64GB storage, phone-call capture, Gmail/Calendar/Slack/Notion context, and an agent that turns raw conversations into follow-ups, briefs, outlines, and priorities. one side is models learning unfamiliar worlds. the other side is memory systems capturing your real one. 2026 AI is getting less like a chatbot and more like an operating layer. bookmark this before “AI memory + agentic reasoning” becomes the only workflow that matters 👇

@MichaelWaitze I agree. the main thing is to move forward

@isofunds When different models win on different benchmarks, what we're really seeing is the job mattering more than the model name. Healthy.

OPUS 5 BUILT THE BEST DRAGON IT EVER MADE — AND THEN BROKE IT this guy benchmarked Opus 5 vs Fable 5 vs Kimi K3 on a blender MCP dragon challenge with identical prompts the bench was clean: - frontier bench: 43.3% (vs 33.7% fable, 34.4% gpt-5.6, 21.1% opus 4.8) - os world 2.0: 70.6% (vs 66.1% fable, 62.6% gpt) - automation bench: 26% (vs 17.4% fable) - arcagi 3: 30.2% (vs 7.8% gpt) the real test: - fable finished dragon in 49 min — previous winner - kimi K3: 3+ hours, 7 agents, incredible detail but rough neck - opus 5: 61 minutes — won on connected anatomy, wings, wings claws near top (the kind of detail that makes a dragon read right) but here's the weird part. at minute 37, opus 5 had already built his best version. the head looked like a dragon. then it kept going for another 24 minutes. the final head looks more like a gecko with horns anthropic's own prompting guide admits opus 5 "verifies its work without being asked. can expand the scope of a task. can waste time through oververification" — meaning their own model knows it does this the dragon gives us real evidence that opus 5 wins on long tool-heavy jobs. and reveals what the benchmark doesn't — sometimes the best iteration is not the final iteration if it's writing code, designing, or 3d modeling — set a stop condition. opus 5 in 2027 needs an editor the way a writer needs one

@crptAtlas 5 minutes and claude code stops being a chore. that's the first honest framing of agent setup ive seen this year

ANTHROPIC JUST SHIPPED A PLUGIN THAT CONFIGURES CLAUDE CODE FOR YOU it is an official one called claude-code-setup and it reads your project then tells you exactly what to turn on it looks at what you are building and points out which hooks, skills, MCP servers and subagents actually make sense for it then it walks you through setting each one up step by step so instead of guessing which automations are worth it you get a real map of your own project you install it with one line /plugin install claude-code-setup@claude-plugins-official 5 minutes and Claude Code stops feeling like a chore bookmark this one before it gets buried

@shiqway92 yes, the price for a good result is sometimes even too high

@isofunds perfection is expensive…

@0xCodez 2 hour workshop from anthropic on graph engineering. free. saves 10 paid courses. that's the actual alpha

Anthropic engineer just released a 2-hour workshop on "Graph Engineering" for agentic systems: “80% of our engineers are using self-improving loops. Now everyone is building agentic graphs.” • 00:00 - Introduction to RAG & Graphs • 06:39 - Core of "Graph Engineering" (state, nodes) • 14:29 - 3 feedback loops of Graph agents • 23:06 - Agent evaluation with Graphs • 36:29 - Agent cycles in graphs • 1:15:22 - Agentic RAG & agent context • 1:41:20 - Evaluation datasets based on Graphs This 2-hour workshop will replace 10 paid courses on agentic engineering. Watch it today, then learn how to become a Graph Engineer in the article below.

@undefinedKi cache hits dropping input from $3 to $0.30 is the sleeper. 10x cost reduction just by keeping system prompt byte-identical

@EXM7777 ads builds campaign paused, waits for go. the only node that should ever require a human

Jensen Huang, CEO of Nvidia, was INSANE for saying this in 2024... "we're all about to be CEOs of AI agents, and the whole skill is briefing workers who do the job better than you can" it was literally impossible back then... not anymore, here's how to build it: we'll use a team of 5 AI workers, one for each department of your business: > content: researches what performed in your niche this month, then drafts the week off the winners > projects: posts the standup every morning, what moved, what is due, what is blocked > outreach: finds businesses that fit your client profile, writes the first touch from the workload it spotted on their own site > finance: drafts the invoices, then a Friday summary of issued, paid and overdue > ads: proposes the media plan, builds the campaign paused, waits for your go the whole setup is in the article below: