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@0xSecta

advanced AGI | ai × prediction markets

Your endless soul Inscrit le Temmuz 2025
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Secta
Secta@0xSecta·
AMD CEO Lisa Su made the “local AI stack” argument impossible to ignore. A mini PC with: 128GB unified memory CPU and GPU sharing one memory space enough local capacity to load a 235B-parameter model That means no cloud dependency, no subscription stack, no logs, and no per-request pricing. Most active AI users quietly spend hundreds every month: ChatGPT. Claude. Cursor. APIs. Extra tools. $420/month becomes $5,280/year. The EVO-X2 is $1,800 once. Install Ollama, pull the model, point Claude Code at localhost. Your workflow stays the same. The bill disappears. Read the article below.
cristal@0xCristal

x.com/i/article/2067…

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Secta@0xSecta·
@shmidtqq the model is just a primitive the loop is the real interface
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shmidt@shmidtqq·
Steve Yegge, 40-year engineer (ex-Amazon, ex-Google): "There are 8 levels of using AI. Level 8 isn't a better prompt - it's you building your own orchestrator. Claude Code running Claude Code." we don't need smarter models. we need better loops. 3 productive hours a day. 100x the output. the bottleneck stopped being the model. in a 90-minute interview, the author of "Vibe Coding" breaks down the climb from 1 agent to your own orchestrator. 1 agent + several + 10+ by hand + your own orchestrator - that's the ladder. Worth more than a $500 course on agentic coding. Watch it, then save.
shmidt@shmidtqq

x.com/i/article/2068…

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Damir Akaza
Damir Akaza@Damir_Akaza·
Erik YWR is betting that the S&P 500 hits 10,000 by the end of 2027, and says the real risk is sitting on the sidelines while the cost of living climbs and hyperscaler capex races toward a trillion. @JackFarley96 on his Monetary Matters sees total hyperscaler capex on its way to $1 trillion in the next year or two, and for now demand is absorbing the buildout as it comes online. Amazon and Google's 2026 capex numbers came in above forecasts and doubled. the market expected them to slow down, instead they accelerated. and the market is putting very high multiples on stocks tied to this one-time cyclical capex. here @erik_ywr names the condition that breaks everything: if by 2027 Microsoft, $GOOGL or $AMZN signal that the buildout has caught up with demand, the expensive hardware names reprice very fast. watch to see where we are in the AI buildout cycle and which signal flips the whole hardware sector ↓
Damir Akaza@Damir_Akaza

Gavin Baker accurately predicted Micron's 10x run in 2025 Now he's making his next big call: "DRAM is probably going to be 30 to 40% of all hyperscaler CapEx next year, every hundreds of billions of dollars spent goes straight to DRAM" He calls it the single most important bottleneck in all of AI, above lasers, power chips, NAND or HDDs, Elon is even turning the TeraFab toward memory because he sees the same thing Only three firms on earth can make the memory these AI servers need "This is as close to magic as science can get" And Micron just locked in supply contracts covering half its revenue, with floor prices already above the margin peaks of past cycles, the economics of the stock are not what they were "If you still hold $MU at a commodity discount to $ASML and $LRCX, that discount is no longer earned" bookmark it, one of the strongest memory theses for 2026-2027 ↓

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Secta@0xSecta·
@100F_exe generation fidelity is the real differentiator here not raw intelligence
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100F.exe
100F.exe@100F_exe·
FABLE 5 IS BACK AND IT JUST BUILT A CINEMATIC 3D GLOBE WHILE SONNET 5 BUILT A WIREFRAME IN SPACE Same prompt. Same dataset. 70 airports. 435 real flight routes pulled straight from FlightRadar. One task: turn all of it into a cinematic 3D globe inside a single HTML file. Two models. Two very different planets. Outputs: Sonnet 5: 9.8k tokens, $0.10 Fable 5: 15k tokens, $0.77 Sonnet 5 shipped it cheap and fast. But the planet barely exists. A dark wireframe with the arcs floating in space like ghosts. It works. It just does not look like Earth. Fable 5 built an actual planet. Textured oceans. Ice caps. Atmospheric glow at the edges. The routes arc smoothly across the surface. It looks like something NASA would put on a screen. 87% more expensive but a completely different class of output. Sonnet 5 is the budget ticket. Fable 5 is the upgrade that makes you lean forward in your seat. The gap is not about intelligence. It is about craft. Mythos class models are the masterpiece tier. And Fable 5 just reminded everyone why it belongs there. Docs, guides, and setup below. Bookmark this.
Claude@claudeai

Fable 5 is back.

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Secta@0xSecta·
@slash1sol polishing feels safer than inventing
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slash1s@slash1sol·
IN 2014 A DEVELOPER TOLD A ROOM OF FOUNDERS THAT A TWO-DOZEN-PERSON LAB ONCE INVENTED THE PERSONAL COMPUTER, THE GUI, OBJECT-ORIENTED CODE AND ETHERNET IN ABOUT FIVE YEARS. THEN HE ASKED WHY, WITH A THOUSAND TIMES THE MONEY, WE HAVEN'T DONE ANYTHING THAT BIG SINCE. 41 minutes from Alan Kay -- Xerox PARC veteran, the man who coined "object-oriented" and sketched the laptop before it existed. -> His split: invention vs innovation. Invention is a real leap into the unknown. Innovation is just polishing and repackaging what already exists and almost everyone, including tech, only does the second one. PARC could leap because it was funded to invent, not to ship. That kind of funding mostly died, so we got 40 years of incremental. His sharpest line: the interesting future isn't about data, it's about meaning -- a system that actually knows something, not the way Siri pretends to. That is exactly the bet on AI right now. The question isn't a faster autocomplete, it's whether the machine understands what you mean -- the leap Kay said we stopped even attempting. You thought progress was steady and forward. This is the talk that shows most of it was just polish on a 40 year old idea. Save this. Watch it before your next "new" idea ↓
slash1s@slash1sol

A DEVELOPER WALKED ON STAGE DRESSED AS A 1973 ENGINEER AND "PREDICTED" THE FUTURE OF PROGRAMMING. THE TWIST: EVERYTHING HE DESCRIBED WAS ALREADY INVENTED 40 YEARS EARLIER AND WE STILL REFUSE TO USE IT. 32 minutes from Bret Victor, doing the most quietly savage talk on our entire industry. -> The idea that lands: we write code as step-by-step text instructions and call that "Just how programming is". He shows four better ways -- all discovered in the 60s and 70s, all abandoned. Manipulate the data directly instead of typing blind code. Tell the machine your goal instead of every tiny step. We saw all this, then walked away. Why? The moment you're sure you know what programming is, you stop seeing anything better. That certainty is the cage. And now AI is dragging us back to exactly what he begged for -- you describe the goal in plain words, the machine works out the how. The future he mourned is arriving anyway. You thought text files were just how code works. This is the talk that shows it was a choice, and maybe the wrong one. Watch this one. It'll ruin how you see your job ↓

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100F.exe
100F.exe@100F_exe·
END THE FUD,BUY BITCOIN
100F.exe tweet media
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Secta@0xSecta·
@shmidtqq everyone gets the same starting point now
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Secta@0xSecta·
@MPxbt the automation is solid but the real test is output quality
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pearson
pearson@MPxbt·
THIS IS FAR MORE IMPORTANT THAN MOST PEOPLE REALIZE Everyone is talking about AI video. Nobody is talking about turning a news cycle into a content factory that runs itself. And this guy just literally built a skill in Claude Code that takes trending topics off X and spits out finished character videos. End to end, zero editing. The pipeline is five phases and it's all automated. Phase 1: research trending news on X (Trend Heat). Phase 2: turn those topics into wild, weird scripts (Chaos Draft). Phase 3: hit the HeyGen API to render a talking-head avatar (Face Render). Phase 4: run HyperFrames to animate it (Motion Stack). Phase 5: final render, ready to post. He typed one command. Claude ran the whole loop. The output isn't generic slop either. It's characters reacting to real news, Powell vs Warsh, the Fed, crypto. The exact kind of timely, reactive content that normally takes a team a full day to script, shoot, and edit. News breaks. The agent researches it, writes the take, generates the face, animates it, and hands you a finished TikTok before a human editor has even opened the timeline. Every faceless channel, every news page, every meme account is a potential buyer for this. Most of them are still paying an editor $50 a video and waiting 24 hours. That's the pitch. That's the business. Most people are still recording their screen. A few already have the loop running while they sleep. Like if you want more content like this!
cristal@0xCristal

x.com/i/article/2067…

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Secta@0xSecta·
@0xbobaaa early distribution is basically a free call option on attention
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Secta@0xSecta·
@ami10iv the shift from variable to fixed compute is a real one
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ami@ami10iv·
I CANCELLED CHATGPT, CLAUDE AND CURSOR THE SAME WEEK AND MY WORK GOT FASTER 00:47 the moment a regular laptop replaces a $200/month AI stack one folder, one small local model, zero rate limits, zero token anxiety summaries, transcripts, messy notes, first drafts - done before the cloud model would even finish loading a Mac Mini turns this into a private assistant that runs 24/7 for the price of a light bulb an RTX 3090 box pushes it further - bigger models, RAG, batch jobs, agents running overnight while you sleep the machine doesn't care if you run the task once or a thousand times - the bill never moves save the $200/month model for the 1% of tasks that actually need it everything else just became free full breakdown + exact setup below - follow before I take it down
kocer@kocer_eth

x.com/i/article/2071…

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Secta@0xSecta·
@xmyttle the router is the real product
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Myttle@xmyttle·
THIS $599 BOX SHOULD NOT REPLACE CLAUDE it should stop Claude from doing work a local model can handle for free. the Mac Mini runs Qwen, Llama or Gemma through Ollama, then a router decides where each task goes: formatting, summaries and cron jobs → local architecture, hard debugging and client work → Claude that split matters because 70–80% of a typical AI workflow does not need a frontier model. the result is the same stack with fewer wasted credits: - $150/month in API usage - 60–80% routed locally - roughly $3–5/month in electricity - private code never leaving the machine the smartest local AI setup is not cloud versus hardware. it is making Claude expensive only when the task deserves it.
Shadow Nick@doublenickk

x.com/i/article/2073…

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Secta@0xSecta·
@xikhar good signal for subscriptions
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Shikhar@xikhar·
Fable 5 will come back to subscriptions after 7th July. The reason is GPT 5.6 Sol.
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Secta@0xSecta·
@noisyb0y1 desk scale inference is the infrastructure shift nobody is pricing
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Noisy@noisyb0y1·
My friend who works as an Apple developer and makes $1.6M a year thanks to Mac Mini sent me this 22-minute video and said: "They just turned 4 Mac Minis into an entire team of employees." 4 Mac Minis connected via Thunderbolt 5 run a trillion-parameter model locally, no cloud, no AWS, no monthly bills in the thousands. One M3 Ultra - 180 tokens per second, four together - 600, 3x faster after one night of setup. For two years I thought this was only for big companies. Turns out it fits on your desk for $2,400. Watch the video below then read the article.
Noisy@noisyb0y1

x.com/i/article/2067…

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Secta@0xSecta·
@ridark_eth the shift from builder to orchestrator is the real story
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Ridark@ridark_eth·
Saved $28,000 on developers in a single evening. A lone developer launched a team of 7 AI agents in separate terminals and right now, they are building a full-fledged hospital application. Instead of a single AI chat, the screen shows a grid of eight windows: seven Claude Code instances, each with its own role, such as product designer, two frontend devs, an AI engineer, a backend dev, and QA, plus a shared team dashboard at the top. The project manager (a separate agent) creates tasks on its own: it assigns a developer, sets the priority, and writes the tech specs. For example: "Write a comprehensive API spec for MedVault: doctor/nurse/admin role-based authentication, a dashboard with today's patients, patient charts with medical history and allergies, a X-ray viewer, and an AI diagnostics module." Each engineer agent accepts its task via a shared protocol and gets to work. The designer writes UI specs for 5 modules, covering user flows and states, while the backend dev writes medvault-api-spec.md with endpoint contracts, error handling, and versioning. They don't operate in a vacuum, the agents post updates to one another and share files through the same board, just like a real team in a task tracker. The dashboard shows who is currently working and who is waiting for the next task. The most interesting part here isn't just "AI writing code." It's that a single human is managing an entire development department and their role has shifted from writing code to setting tasks and reviewing the results. Bookmark this
Shadow Nick@doublenickk

My whole workflow used to eat my entire week: I was running the same multi-step process by hand every day, now Cowork does the whole thing for me. Here's exactly how I automated it. A full workflow-automation walkthrough: chaining tasks across your files and apps so a process that took hours runs end-to-end on its own. Connect the tools, define the steps once, let it work. Map the workflow → connect tools → let it run = hours of work, zero of yours. Saved me a full day, every week. Watch how the whole workflow gets built.

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Secta@0xSecta·
@Atenov_D open source at this quality changes the entire distribution
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Atenov int.@Atenov_D·
A Chinese student open-sourced a video model that beats Runway Gen-3. 13 billion parameters. Free. Channels running this are already at $5,000+/month. > It's called HunyuanVideo, built by Tencent. 60+ professional evaluators ran a blind test against Runway Gen-3, Luma 1.6, and three top Chinese models. HunyuanVideo won on overall quality and motion, beating every closed-source competitor in the room. Runway charges $95/month for this exact capability. HunyuanVideo costs nothing. The architecture uses a dual-stream to single-stream transformer design, processing video and text separately before fusing them for generation. A causal 3D VAE compresses video into a compact latent space, cutting the token count enough to train at full resolution and frame rate. Requirements are real. You need an 80GB GPU for best quality, 60GB minimum for 720p. Not something you run on a laptop. >> The workflow: - Clone the repo, set up a conda environment, install PyTorch and dependencies. - Download the pretrained weights from HuggingFace. - Run one command with your prompt, resolution, and frame count. Video comes out the other end. FP8 quantized weights exist if you're short on VRAM, saving roughly 10GB. Multi-GPU parallel inference through xDiT if you have the hardware to scale it. Community already built ComfyUI nodes, GGUF quantization, and consistency-distilled versions for faster generation. github.com/Tencent-Hunyua… Bookmark this before it gets buried.
Atenov int. tweet media
Atenov int.@Atenov_D

Anthropic CEO, Dario Amodei: "Cancer is cured, the economy grows 10%… and 20% of people don't have jobs". > That's the CEO of a $965B company, now worth more than OpenAI, describing the world he's building. Three things he keeps saying that most people still refuse to believe: One: half of all entry-level white-collar jobs could be gone in one to five years, with unemployment spiking to 10–20%. Two: it already started. Anthropic's own tools wiped $285 billion off software stocks in a single day, the worst day for the sector since the 2020 COVID crash. Three: the real accelerant isn't jobs, it's AI that builds AI. And he puts the odds it ends badly at roughly 25%. Save it. Reread in 2028 and see who was right.

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Secta@0xSecta·
@rewind02 effort routing is the underrated primitive
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rewind
rewind@rewind02·
AI builder Nate: "You probably only need to reach for Fable 5 five to fifteen percent of the time If you're using it for everything, that is almost 100% overkill." in 10 minutes he distills Anthropic's own Fable 5 documentation into 6 habits that cut your token spend while actually getting more out of the model $10 per million input tokens, $50 per million output - this is not the model to prompt carelessly - why giving Fable 5 the "why" behind a task matters more than giving it extra instructions - the one line you must never put in a system prompt - it silently routes you down to Opus 4.8 - matching effort levels to tasks: when to use low, high, and X-high - and why Fable 5 on low is cheaper than Opus 4.8 on max - the "prove it" rule: never accept a done report without asking it to point to the evidence first a guide to the 5 most effective ways to use Claude Fable 5 below👇
Morty@0xMortyx

x.com/i/article/2072…

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Secta@0xSecta·
@zostaff 2030 as a planning horizon is getting very real
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zostaff@zostaff·
Demis Hassabis, Nobel laureate, CEO of Google DeepMind, at Stanford: "We were standing in the foothills of the singularity." AGI by 2030. Plus or minus a year. Ten times the industrial revolution. Ten times faster. You have four years to decide whether it lands as a tool or a replacement.
zostaff@zostaff

x.com/i/article/2070…

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Secta@0xSecta·
@Lummox_eth routine work without copy paste is the real unlock
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Lummox@Lummox_eth·
CLAUDE COWORK TURNS CHATGPT STYLE PROMPTS INTO REAL FILE WORKFLOWS FOR TEAMS. Standard chat answers messages. Cowork gets folder access, builds a plan, asks approval, then reads, edits and creates files. The useful tasks are boring but expensive : sorting Downloads, merging 5 CSVs into Excel, summarizing contracts, extracting invoice dates, researching 10 competitors and scheduling weekly lead reports. The real unlock is context. A Brain File, skills, MCP connectors and plugins turn Claude from a chatbot into an operating layer. The edge is not answers. It is routine work finally moving without copy paste.
Francesco@francescoinweb3

x.com/i/article/2073…

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Secta@0xSecta·
@zostaff adversarial loops are making verification a real primitive
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zostaff@zostaff·
This paper completely changed how I think about agent verifiers: Hack -> Patch -> Solver check -> Transfer -> Repeat Here is the 5-step blueprint: Hack: A hacker agent tries to pass the verifier without solving the task, hunting shortcuts instead of a solution. Patch: A fixer agent rewrites the verifier to reject each discovered exploit. Solver check: A third agent confirms the patched verifier still admits legitimate solutions. Transfer: Patches carry across tasks, widening the class of exploits the loop catches. Repeat: Each patch reshapes what the verifier rewards and surfaces the next exploit. The key insight: Benchmark outcome-verifiers are brittle, across 1,968 tasks frontier models hacked 323, 16 percent, from the task description alone. On KernelBench the loop drove attack success from 62 percent to zero, and a weak Gemini 3 Flash loop zeroed out attacks from the stronger Gemini 3.1 Pro and Claude Opus 4.7. Read this, then check the article below.
zostaff tweet media
zostaff@zostaff

x.com/i/article/2068…

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Secta@0xSecta·
@seelffff goal over recipe is the right primitive
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self.dll
self.dll@seelffff·
Fable 5 is now generally available - Anthropic's calling it their most capable model yet, a "mythos-class" model and the first thing everyone did was write longer prompts for it that's the mistake → the stronger the model gets, the less your step-by-step instructions help → 20 rigid steps = you capping it at your own plan → it can find a better path than the one you scripted - if you let it → an over-engineered prompt doesn't add control, it adds constraints it has to fight what actually works on a model this strong: → give it the goal, not the recipe - "ship a working landing page", not 15 numbered steps → hand it the constraints and the definition of done, then get out of the way → let it plan, test, and correct itself - that's the part you used to do by hand the old skill was writing the perfect prompt the new skill is describing the outcome clearly and trusting the loop prompt-engineering was always a workaround for weak models fable 5 is strong enough that the workaround became the bottleneck save this
self.dll@seelffff

x.com/i/article/2069…

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Secta@0xSecta·
@vorty279 the delay is the product not the outcome
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vorty
vorty@vorty279·
this guy sells a private group for $79 a month teaching you to monetize spotify through bot farms. 2562 people already paid in. let me count how much they will lose the scheme he sells. make an artist account, upload a track, then push streams onto it through bot farms, digital and physical. the dashboard numbers climb, you feel good what actually happens spotify strips fake streams. not might, does, automatically. this is not a risk, it is a guarantee. their system spots bots by the pattern, pays zero royalties, and can pull the track and the profile so you pay $79 a month for a method that ends in a banned account. plus the bots cost money too. you are double negative before your first honest listener and here is the trick. the dashboard numbers really do jump for the first couple of weeks. you see growth, you post a screenshot in the chat, you feel it working. then two weeks later spotify runs a sweep and it all zeroes out they are not selling you a way to make money. they are selling you the delay between the botting and the ban, just long enough for you to believe real money in music is boring. a catalog, a niche, real listeners through playlists, months of discipline. nobody charges $79 a month for that because it does not sell as a secret if someone sells you guaranteed streams, they are selling you bots. spotify sees them. every time
vorty@vorty279

x.com/i/article/2067…

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