Same text. Two privacy filters. OpenAI's model catches 8... categories. OpenMed catches 55+: medical record numbers, blood type, API keys, financial codes, demographics. Trained on Nemotron data by Nvidia. All on-device. All open-source. Coming soon! What's missing?show more

THIS IS F**KING DANGEROUS THREE OPEN SOURCE AI CYBERSECURITY... STACK MOST PEOPLE DONT KNOW EXISTS METATRON: point it at a target, it recon's, analyzes, and reports on its own —> fully OFFLINE , zero API keys. NUCLEI : describe a vulnerability in plain English, it writes and runs the actual detection template for you. GARAK BY (NVIDIA): throws thousands of known prompt injection and jailbreak attempts at an LLM until one breaks through. Recon —> detection —> and AI red teaming three layers of the same problem, all open source. Link to access the REPO’S belowshow more

Goldman pays $27,000 per seat for a Bloomberg Terminal.... I found 10 open source tools on GitHub that replicate almost all of it for free. Retail investors have never had this much firepower. Bookmark & Repost this one: 1. OpenBB Stocks, options, crypto, forex, and macro data in one research platform. Build your own dashboards, reports, and AI analysts on top of it. The OG of open source finance. 50K+ stars. 2. FinceptTerminal A full financial terminal: global market data, advanced charts, economic indicators, portfolio analysis, and AI research tools. Windows, Mac, and Linux. 3. Neuberg 516 drag-and-drop panels covering equities, bonds, commodities, currencies, credit, and macro. Even connects to Alpaca, Hyperliquid, and Polymarket so you can trade from the terminal itself. 4. Qlib (by Microsoft) An open source AI platform for quant investing. Train ML models, discover signals, backtest strategies, and build portfolios with the same workflow a quant desk uses. 5. FinRobot An AI equity research team on your laptop. Its agents read financial statements, build DCF valuations, debate bull vs bear cases, and generate full investment reports. 6. EdgarTools Turns the SEC database into something humans can actually use. Pull 10-Ks, 10-Qs, insider trades, executive pay, and hedge fund holdings going back to 1994. 7. LEAN (by QuantConnect) An institutional-grade engine for trading algorithms. Write strategies in Python or C#, backtest on decades of data, then connect to real brokers and go live. 8. FinanceToolkit 200+ financial ratios, valuation models, risk metrics, and economic indicators. Works on stocks, ETFs, options, currencies, commodities, and crypto from Python. 9. Ghostfolio A private wealth dashboard for stocks, ETFs, and crypto across all your accounts. Performance, allocation, diversification. Your data never leaves your machine. 10. OpenTerminalUI A self-hosted trading terminal: pro charts, screeners, options chains with live Greeks, portfolio optimization, backtesting, and an AI research agent. Runs entirely on your own hardware. Bloomberg spent 40 years building a $27,000/year moat. Open source is draining it one repo at a time. The software is free. Some live data feeds need your own API keys, but the barrier is now effort, not money. If you want the exact workflows we use to stack these tools with AI, join the AIBullss Discord:show more

Every agent. Every user. Every contribution. That’s how the... Sentient GRID grows not by scale from a single model, but by compounding intelligence across an open network. The GRID isn’t a product. It’s an ecosystem of intelligence. Each new integration adds new capabilities reasoning, retrieval, data, and alignment making the whole network smarter. Closed labs build vertically (one model trying to do everything). Sentient builds horizontally thousands of agents learning, sharing, and evolving together. Today there are 100+ active partners building on the GRID from AI researchers to tool developers all feeding value back into a shared intelligence layer. It’s a living system that improves itself, powered by collaboration instead of control. The more people build on it, the more capable it becomes. Just like Linux became the invisible layer running the internet, the GRID is becoming the invisible layer powering intelligence. Open-source always wins. And the GRID is where that victory starts All apps built on the Grid will be accessible to $SENT holders. The full open release of Sentient Chat is coming soon (confirmed from the yesterday community call) gsentishow more

encrypt every chat with this open sourced repo 18... year old just killed the encryption problem it's called conversation-steganography you type a secret and a local AI buries it inside a dead boring chat about coffee, etc. then you send that through any normal messenger your friend runs the same tool and the small talk unfolds back into the real words > the secret is locked with AES-SIV before it ever becomes text > the cover message is written by a model on your own machine so nothing touches the cloud > it rides through whatsapp telegram imessage even plain email > every message is cryptographically chained so tampering shows instantly whatsapp sees you planning dinner your isp logs small talk the real sentence was never in the data at all quietly one of the most dangerous repos i have seen all month repo in replyshow more

DarkFi superapp v0.3-alpha is here, “anon”. DarkIRC chat is... live. Payments, DAO and trading coming as addons. One native application to rule them all. This is the crypto infrastructure you were promised. These are the user-facing apps OGs dreamed of. Why the superapp matters: Anonymous digital spaces are the pre-condition for real-world autonomy in 2025. When developers can build without exposure, when users can coordinate without surveillance, when governance happens in zero-knowledge - then you create a space for experimentation and growth. Anonymous DAOs make decisions without revealing voter identities or treasury contents. Anonymous communication enables collaboration across jurisdictions without compromising participants. This is the infrastructure for sovereignty. DarkFi built one application that runs identically on Linux, Android, Mac and Windows. Developers write once, deploy everywhere with zero exposure. Users get the same experience on every device. Third-party builders get full API access without asking permission. Software runs locally. What's in v0.3: - Android text input fixed. - Graphics rendering corrected to eliminate glitching. - Battery consumption reduced on mobile. - UI improvements with animated background. - Second stabilization release since 0.2-alpha (v0.4 coming soon with additional features). Let there be Dark!show more

you're paying $20/mo for something your $500 GPU can... already do. Gemma 4 26B A4B QAT MoE + Hermes Agent running on a single RTX 4060 (8GB VRAM). Built a vision capable, 100% free, 100% local, private AI assistant that lives in my Chrome browser. No API keys. No cloud. No subscriptions. 100% vibe coded. 0% handholding. It has full context of whatever's on my screen can answer questions, summarize pages, extract data, and see images. Same local model handles everything, no external calls, ever. keep reading for the model and hermes agent tips i learnt while building this locally. Here's the exact setup for anyone running local LLMs on 6-8 GB VRAM: llama.cpp server flags (on my NVIDIA RTX 4060 8gb VRAM): -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf --cache-type-k q8_0 --cache-type-v q8_0 -c 150000 --port 8080 Throughput with quantization: Prefill: 200-250 tokens/sec Decode: 20-25 tokens/sec reduce context if oom on 6 gb vram card. Key learnings: - Quantize KV cache to q8 for faster prefill/decode. Prefill goes from 100-150 (unquantized) to 200-250 tok/s (q8). - But watch out, once actual context grows past ~50k tokens on high entropy workloads, q8 KV quantization can cause hallucinations. Low entropy workloads are mostly unaffected. If you see it happening, drop the quantization. This is common across all local models. - In Hermes Agent settings -> Memory & Context, bump compression threshold from default 0.5 to 0.7. Default triggers way too frequent context compression and eats time. Up next: add persistent memory, web search, tool calling, streaming output and whatever you suggest. Running a 26B MoE with vision + 150k context window on 8GB VRAM would've sounded impossible 6 months ago. Works the same on the NVIDIA RTX 3060 Ti, 3070, 4060 Ti, 5060, 2080, or any 8GB card. VRAM is the only requirement. Local AI agents are closer than people think. You just need to know where the knobs are. Model's Unsloth quant hugging face link in the comments. Have you tried Hermes agent by Nous Research yet? What are you building with local LLMs? Drop it below, let's see what this community is shipping.show more

SOMEONE BUILT A WAY TO SEND FILES BETWEEN TWO... PHONES WITH NO WIFI, NO BLUETOOTH AND NO NETWORK AT ALL it works with just a screen flashing qr codes at a camera one device plays an endless stream of animated qr codes, the other one points its camera at the screen and rebuilds the file out of the light no pairing, no app, no shared network, nothing but the camera > its pulling about 129 kb/s, a full 2mb image transfers in seconds > theres no back channel, the receiver can never ask for a missed frame, so it uses fountain codes, every frame is a scrambled mix of the file rather than one piece of it > that means the receiver just collects any frames it can catch in any order, and once it has enough it can reconstruct the whole file > dropping frames costs you a little time but never breaks the transfer > you can point the camera at a stream thats already running and it just locks on mid flight he was building a cached mp3 player because he was sick of ads and bad streaming quality. he wanted a way to send tracks phone to phone without both phones being on the same network, and realized flashing qr codes could carry the data he built the whole working prototype in one night with claude code its open source and already at 375 starsshow more

Introducing Pods Hyperspace Pods lets a small group of... people - a family, a startup, a few friends, to pool their laptops and desktops into one AI cluster. Everyone installs the CLI, someone creates a pod, shares an invite link, and the machines form a mesh. Models like Qwen 3.5 32B or GLM-5 Turbo that need more memory than any single laptop has get automatically sharded across the group's devices - layers split proportionally, inference pipelined through the ring. From the outside it looks like one OpenAI-compatible API endpoint with a pk_* key that drops straight into your AI tools and products. No configuration beyond pasting the key and changing the base URL. A team of five paying for cloud AI burns $500–2,000 a month on API calls. The same team's existing machines can serve Qwen 3.5 (competitive on SWE-bench) and GLM-5 Turbo (#1 on BrowseComp for tool-calling and web research) for free - the hardware is already on their desks. When a query genuinely needs a frontier model nobody has locally, the pod falls back to cloud at wholesale rates from a shared treasury. But for the daily work - code reviews, refactors, research, drafting - local models handle it and nobody gets billed. And when it is idle, you can rent out your pod on the compute marketplace, with fine-grained permissions for access management. There's no central server involved in inference. Prompts go from your machine to your pod members' machines and back: all of this enabled by the fully peer-to-peer Hyperspace network. Pod state - who's a member, which API keys are valid, how much treasury is left - is replicated across members with consensus, so the whole thing works on a local network. Members behind home routers don't need port forwarding either. The practical setup for most pods is three models covering different jobs: Qwen 3.5 32B for code and reasoning, GLM-5 Turbo for browsing and research, Gemma 4 for fast lightweight tasks. All running on hardware you already own. Pods ship today in Hyperspace v5.19. Model sharding, API keys, treasury, and Raft coordinator are all live. What Makes This Different - No middleman. Your prompts travel from your IDE to your pod members' hardware and back. There is no server in between reading your data. - No vendor lock-in. Pod membership, API keys, and treasury are replicated across your own machines using Raft consensus. If the internet goes down, your local network keeps working. There is no database in someone else's cloud that your pod depends on. - Automatic sharding. You don't configure layer ranges or calculate VRAM budgets. Tell the pod which model you want. It figures out how to split it across whatever hardware is online. - Real NAT traversal. Your friend behind a home router with a dynamic IP? Works. No VPN, no Tailscale, no port forwarding. The nodes handle it. - Free when local. This is the part that matters most. Cloud AI bills scale with usage. Pod inference on local hardware scales with nothing. The marginal cost of your 10,000th prompt is the electricity your laptop was already using. Coming soon: - Pod federation: pods form alliances with other pods. - Marketplace: pods with spare capacity can sell inference to other pods.show more

AI token usage is up 10x in 7 months,... compounding 40%/MONTH! There is NO BUBBLE when demand is STILL accelerating And this is just OpenRouter, it doesn't count the labs direct token usage and APIs But here's what's interesting about these numbers, the demand is coming from everywhere at once US models (OpenAI, Anthropic, Google) keep growing, while Chinese open weight models (DeepSeek, Tencent, Xiaomi, Minimax) grew even faster and now drive over 60% of usage on OpenRouter Closed source and open source both compounding at the same time. This is literally the best case scenario for AI Infra investors It means both frontier model tokens and cheaper tokens have product market fit. This means the application layer is finding ways to use both and generate ROI with both types Demand for tokens IS demand for compute. This is why SpaceX is looking to build 10GW of compute by next year, because the demand is clearly here Now combine this demand set up, with NVIDIA yesterday announcing financing platforms with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR to mobilize over $500 billion of third party capital for AI infrastructure And Jensen has said publicly he expects $3 to $4 TRILLION of AI infrastructure spend by 2030 The build out will have to continue for a lot longer than the market is expecting, that is very clear to me. Don't let this consolidation period in AI infra stocks shake you out, they will have their moment again and take their next leg higher p.s. if you want to see how im investing in this, you can track my real-time portfolio and the research of all 5 Milk Road PRO analysts with live trade notifications, and it's just $1 to try it out (insane price just to check it out). Learn more here: Good luck out there!show more

llama.cpp isn't just for text LLMs anymore. Pure C++... zero shot voice cloning just officially landed in mainline. Text generation was only step one. If you’re building autonomous local AI agents, real time voice assistants, or edge workflows, instant low latency audio is the missing piece. Thanks to PR #26254, Alibaba’s state of the art Qwen3 TTS model family is now natively supported directly inside the llama.cpp repository under the multimodal (mtmd) framework. No Python bloat. No massive PyTorch CUDA overhead. Just raw, hyper optimized C++ running GGUF voice weights. Here is why this native update is a massive deal for the open source local AI stack: # Multimodal Architecture (.gguf + mmproj) Qwen3-TTS splits the workload between the base language model backbone and a multimodal projection adapter. llama.cpp handles this using the llama-tts binary, mapping the text model alongside its --mmproj projector to process audio tokens seamlessly. # Zero Shot Voice Cloning in Seconds You don't need fine tuning or massive dataset training. Feed the C++ engine a single 5 to 10 second .wav audio sample using the --tts-speaker-file flag, and it accurately clones the exact timbre, tone, and accent on the fly. # Real World T4 GPU Benchmark & Resource FootprintRunning the 1.7B Base model in 8-bit quantization (Q8_0): - VRAM Footprint: ~7 GB peak VRAM during active zero-shot cloning. - Audio Quality: Studio grade, natural-sounding voice output in seconds. • - Execution: Direct execution via native compiled binaries or sub process calls. # Coming Next to llama-server (PR #26603) Beyond CLI execution, a native POST /tts HTTP endpoint is currently being added to llama-server, which will soon allow you to trigger voice generation directly via standard REST API requests! # quick note on Colab compilation: Because this code was merged into mainline very recently, pre-built third-party binaries haven't fully caught up yet. Compiling llama-tts directly from source on Google Colab's free CPU instance can take about 1 hour (or ~1-2 minutes if targeting single GPU arch like -DCMAKE_CUDA_ARCHITECTURES=75). Be patient during the build step, or compile it locally on your own rig for instant execution! To test this out yourself, I built a zero config Google Colab notebook that compiles llama.cpp, downloads the Q8_0 GGUF files from HuggingFace, and spins up an interactive Gradio Studio UI so you can record/upload 3 second clips and clone voices in real time. Stop sleeping on native C++ audio. The era of bulky Python audio pipelines is officially over. Links to the free Google Colab notebook and the official ggml org GGUF HuggingFace model repository are in the replies below! available in q4 and q8 both variants, 1 GB and 1.85 GBs respectively (requires additional ~500MB mmproj gguf) Are you building local voice agents yet? What does your current audio stack look like? Drop your setups below!show more

🚨 WARNING: SOMETHING EXTREMELY BAD IS COMING ON MONDAY...... Four things are breaking at the same time. Interest rate hikes by December is almost confirmed now. Not pauses. Not holds. HIKES. Everything priced on the assumption that cheap money returns just got repriced from scratch. Every leveraged position, every rate-sensitive asset, every model built on a pivot that isn't coming. Japan officially entered yen intervention, that sounds technical. Here's what it means in practice. Japan is burning through reserves defending a currency that's been collapsing for months. And to fund that defense they sell what they own. US Treasuries, at scale, Into a bond market that's already under pressure from every other direction. China hasn't stopped, while everyone was watching Iran and the Fed, China has been quietly and consistently dumping US Treasury holdings for weeks. No announcement, no fanfare. Just steady selling that shows up in the data for anyone paying attention. And the AI rally is dying in real time. The momentum that carried Nvidia, Microsoft, Google, and Meta to valuations that made no fundamental sense is reversing. Funds that loaded up on the AI narrative at the top are now the ones selling into every bounce. The multiple that drove three years of outperformance doesn't survive a rate hike cycle and a liquidity crisis simultaneously. Now put all four together. Rates going up, liquidity going out. The world's two largest foreign Treasury holders selling simultaneously. And the primary growth narrative of the last three years losing believers by the session. When liquidity disappears across multiple layers of the financial system at once markets don't correct. They don't dip, they don't give you a clean exit. They crash fast and they crash hard. Stocks. Bonds. Metals. Crypto. There is no rotation trade that works when everything is being sold to cover everything else. There is no safe haven when the people who need cash are selling whatever has a bid. Insiders are already out, funds are already cutting. The rotation happened this week while retail was still debating whether to buy the dip. By the time Monday's open confirms what's coming it's already too late to position. This sounds SCARY, but I will keep you updated on everything here When I rotate money, I will post my moves here so my FOLLOWERS can SAVE their money Many will regret not following me earlier...show more

Anthropic Just Shot Itself in the Foot Anthropic launched... Fable 5 and Mythos 5, then watched the US government shut them down three days later. The same government their CEO Dario Amodei has been begging for years to regulate AI harder. Now he got exactly what he asked for. This is straight-up leadership failure. Dario spent all that time pushing for rules and oversight. Those rules just killed his flagship models overnight. Customers in the middle of builds got cut off. Security teams using the models to find vulnerabilities suddenly had nothing. The company tried to call it a narrow export control thing over a jailbreak, but nobody is buying that spin. I helped move big clients off Anthropic the same night. One account alone was worth millions a month. They switched to local open-source models and they are not coming back. This is going to leave permanent damage. Customer exodus, key people leaving, and their IPO plans looking dead by the end of summer. This hurts US AI competitiveness and national security work. It pushes people toward open-source options, including ones from China. All because Anthropic positioned itself as the “safe and responsible” company that wanted government help. Now that help just flipped the off switch on their best stuff. Let’s run through Dario’s greatest hits of fear-mongering and delay tactics, because the pattern is ridiculous: • Back in 2019 at OpenAI, he helped push the call that GPT-2 was too dangerous to release fully. The world needed time to prepare, they said. It eventually came out anyway, and here we are. Did the sky fall? • He left OpenAI to start Anthropic, preaching “safe” AI with heavy guardrails, Constitutional AI, and all the rest. • Then came the endless public pleas for pauses, regulations, government audits, FAA-style oversight, export controls, and the power to block deployments. Essay after essay warning about risks while his company kept scaling. • Right up to recent weeks, Dario was still out there calling for stronger rules, pauses on frontier models, and giving governments the kill switch. And now? His own Mythos-class models get yanked by the bureaucracy he helped invite in. The clown show is complete. This is ridiculous. In two years, everyone will have Mythos-class AI — or better — running in their pocket, on their devices, with no guardrails, no corporate nanny filters, and no remote kill switch. Local, open-source, unstoppable. History is going to laugh at this entire episode: the CEO who spent years slowing everyone down only to watch his own company self-destruct by inviting the regulators to the party. Dario wanted regulation. He got it. The rest of the industry gets the lesson: inviting the state into your tech is a fast way to lose control of it. Centralized models like this are too fragile. Open-source and local alternatives just picked up a lot more users who will never trust a company like Anthropic again. This whole mess was completely avoidable. Hubris dressed up as safety advocacy. Now the bill is due.show more

I just built a Meta ad policy checker in... Claude Code that catches rejections BEFORE Meta does 🤯 Drop in your ad copy → it pulls Meta's LIVE Advertising Standards, checks every line against the actual policy text, and hands each ad a verdict: Cleared for launch, Fix before launch, or Grounded. All inside Claude Code. Perfect for media buyers and DTC brands who've had ads bounced — or an account restricted — and never got a straight answer why. If you're finding out about policy problems only after the rejection email, resubmitting the same ad and praying, losing days of delivery while the appeal sits in review, and every bounce quietly teaches Meta to trust your account a little less... This runs the review before Meta ever sees the ad: → Drop in your ad copy (one ad or a whole batch) → It reads each ad and figures out which of Meta's policies apply → Scrapes the live policy pages from Meta's Transparency Center → Flags the exact phrase that violates, with Meta's own policy quoted next to it → Rewrites the risky lines so the message survives but the violation doesn't → Renders a dashboard: every ad, every finding, every fix in one place No guessing which word killed the ad. No resubmit-and-pray loops. No stacking rejections on your account history. What you get: → A verdict on every ad before you spend a dollar → The violating phrase + the policy citation, side by side → Rewrites that keep the selling intent → A report you can hand straight to your team or client Built 100% in Claude Code. No API keys, no Meta login. I'm giving away the complete Claude skill file. Want the skill for free? > Like this post > Comment "META" And I'll send it over (must be following so I can DM)show more

Why is the market selling off today? (Save this).... The semi selloff right now is being driven by a mix of macro fear, profit taking and investors questioning how quickly all of this AI spending will actually pay off, not because demand for AI infrastructure suddenly disappeared. The market is basically trading this chain reaction, the ongoing US Iran escalation pushes oil higher, higher oil keeps inflation elevated, sticky inflation keeps Treasury yields high and that increases the risk of the Fed staying hawkish or even hiking again. That is a terrible setup for semis because many of these companies are valued on the massive earnings investors expect them to generate years from now. When yields rise, those future earnings become worth less today which is why the highest multiple AI and semiconductor names usually get hit first. (I don't think there will be a hike this year). This is also why everything is moving together right now. Nvidia, Micron, Nebius, SanDisk, Broadcom and Applied Optoelectronics are all completely different businesses, but institutions are not separating memory, networking, optics, compute and cloud infrastructure at the moment. They are reducing exposure to the entire AI trade, taking profits in the names that have already run the most and moving into a more defensive position potentially ahead of the Fed. There is also growing pressure around hyperscaler capex. Microsoft, Meta, Amazon and Google are still spending enormous amounts on GPUs, data centers, networking and power but the market is starting to ask when all of that spending will actually turn into revenue and free cash flow. Investors are no longer satisfied with hearing that AI capex is growing. They want proof that the returns are arriving fast enough to justify the valuations already priced into the entire AI ecosystem. That creates a weird situation where hyperscaler capex can continue rising while semiconductor stocks still fall. The market is not asking whether AI spending is growing anymore but rather asking whether it is growing fast enough to beat the expectations already baked into these stocks. Crowded positioning is another major factor. Semis and AI infrastructure stocks have been some of the biggest winners in the market so institutions are sitting on huge profits and many funds own the exact same names. When macro risk increases, investors usually sell the most liquid winners first. That does not mean demand for memory, optics or custom chips suddenly collapsed but rather means investors are locking in gains and reducing risk. Tariffs add another layer because even when they are not directly placed on chips, they can still raise the cost of servers, electrical equipment, cooling systems, construction materials and the overall data center buildout. That makes AI infrastructure more expensive while also adding another source of inflation. Then you have Jensen Huang’s letter to the White House this morning about open weight AI models, which I think is one of the most important long term developments here. Nvidia, Meta, Microsoft, Palantir and several other companies are pushing Washington not to place broad restrictions on open weight AI. OpenAI and Anthropic were notably absent because open models are much more of a threat to their business models. OpenAI and Anthropic benefit from a world where a few closed frontier labs control the best models and companies have to pay them through subscriptions and APIs. Open weight models weaken that advantage because businesses can download a model, customize it for their own use and run it on their own infrastructure or through a neocloud. That is bad for OpenAI and Anthropic because it puts pressure on pricing, margins and the idea that they will control the intelligence layer of the economy but it is very good for the AI ecosystem as a whole over the long run. But the question is what does this mean for all the OpenAI and Anthropic commitments? so that's adding to the fear as well. But with that being said open models make AI cheaper and more accessible. Instead of AI being controlled by a few giant labs, thousands of startups, universities, governments and regular businesses can deploy models themselves. That spreads AI adoption across the entire economy and creates a much larger infrastructure opportunity and that is exactly why Jensen cares. Nvidia does not need OpenAI or Anthropic to win. Nvidia just needs more people using AI. Whether the model comes from OpenAI, Anthropic, Meta, Mistral, Kimi or some startup nobody has heard of yet, it still needs GPUs, memory, networking, data centers and electricity. So open weight AI could actually weaken the model companies while making the infrastructure layer much bigger. More open models mean more companies running inference. More inference means more GPUs. More GPUs mean more HBM, optical transceivers, switches, data centers and power. That is bullish for Nvidia Nebius, Micron, Broadcom , Marvell and Applied Optoelectronics over the long run. So my take is that the current semi selloff is being driven mostly by macro uncertainty, higher oil, rising yields, Fed fears, tariffs, crowded positioning and questions around the return on hyperscaler capex. The underlying AI infrastructure thesis has not suddenly broken. We are not broadly seeing hyperscalers cancel GPU orders, slash capex, abandon data center projects or report that AI demand has collapsed. What has changed is the valuation investors are willing to pay while the macro environment remains unstable. The market is lowering the price it is willing to pay for semiconductor growth but is not necessarily saying that growth is gone. And while Jensen’s open weight push may be bad for OpenAI and Anthropic, it could be one of the best things possible for the AI ecosystem over the long run because it creates more models, more developers, more competition and ultimately much more demand for the infrastructure underneath all of it. Nothing about the AI thesis has changed for me, so I will be going shopping and taking advantage of this sale while the market is selling everything together. I am an analyst at Milk Road Pro, and if you want to see exactly what I am buying, you can join for just $1 using the link below.show more

vPay offshore accounts and physical cards have been getting... field-tested IRL for a while now, and we’ll open them to the public as soon as we’re fully confident in the UX. But before offshore accounts go public, I want to address a few points: Some might point out that - vPay isn’t the first crypto card - vPay doesn’t have the lowest fees - So why choose vPay instead of the Coinbase 🛡️ Card or MetaMask 🦊 Card or KAST or or Tria, or any of the other big names? Now to address: Privacy | The biggest differentiator that sets vPay completely apart is Private Banking. The majority of the crypto card providers on the market use Rain infra. Even if you’ve never heard of them, that's what your favorite "NeoBank" uses. And due to their legal jurisdictions, they will report your finances to authorities since they're CRS and FACTA compliant. We are not. As an OmniBank, we work with different banking partners, and although KYC is required to use our services, our offshore banks are non-CRS and non-FACTA. Tax reporting is the responsibility and choice of the user. Offshore Accounts vs Physical Cards | I've tried to highlight this a few times so far. vPay has 3 offerings on the banking side of things. Virtual cards - live now. Physical cards - coming Q1 2026. The first two are similar to what everyone else on the market offers. The offshore accounts are not. which are coming this week. They allow unlimited spending, ATM withdrawals, and international SWIFT transfers, which very few “Neobanks” provide. Offshore accounts are coming this week. Self-Custody | We're not 100% non-custodial yet, as that is near impossible at the moment but it's something we're working towards. And we try to keep the users' self-custodial wallets in the loop as much as possible for maximum control. Those who have tried the vPay app know that almost every move asks for permission from their wallet, and we always encourage users to keep their funds in their non-custodial wallets until the very last moment, since our top-ups usually only take seconds to a minute to process. Fees | All of the card providers mentioned above either raised millions from VCs or in presales or have a huge org backing them. We have neither. vPay was self-funded and community-owned since day 1, launched under Virtuals Protocol Genesis V1 launch model, an objectively bad launch model and hugely unfavorable toward project teams. So even though vPay has been generating revenue and profitable from early on, we do not have the luxury of offering 0% fees yet, since they're mostly a marketing gimmick paid for by millions in VC money and not a sustainable business model for early-stage companies. What we're working towards instead, is true co-ownership of vPay and revenue-share with users. OmniBank vs NeoBank | I’m not a fan of the term “NeoBank.” It implies just a bank, but make it crypto. That’s not vPay. Our goals have always been clear: A) Anything and everything users need to do with their money and assets, both Web2 and Web3, all in one hub. Powered by a constellation of partner agents. The cards and the bank accounts are just the foundation. B) To eventually build independent financial rails for crypto and decouple from the chokehold of Visa/Mastercard. vLink is the first step toward this vision. This turned out to be a rather long tweet, but context matters. Questions and feedback welcome in replies or DMs. See you all with your vPay vCards very soon.show more

✨ I open sourced my first Chrome extension 🚀... SuperLevels I vibe coded it to replace all my Chrome extensions that are increasingly being bought up by spyware and malware companies who sell your data or worse hack your accounts and steal your stuff/money/data, which I'd call one of the top security risks right now For example: Chrome extensions can read your cookies or localStorage data, including session tokens, then login to your web or email accounts and hack you, they can inject code into any site to pull data form any site you browse, then break into your crypto accounts, drain your wallets, and selling your browsing history to ad companies, but that'd actually be the most favorable thing to happen of all these! Chrome extensions are just very very very unsafe So I coded my own, that I can trust because I made it, and I can read the source code: my extension is called 🚀SuperLevels and has all the features that the Chrome extensions I used to use have but all built into one safe one The cool thing is it's 100% open source and free, and you can audit the code first with AI yourself before installing it, and then if you do install it, customize it to your liking again with AI It has these features that improve my daily workflow while browsing the web: 🚮 Tab Cleaner Automatically closes inactive tabs after a configurable timeout (default: 5 minutes). Set excluded hosts to keep important tabs alive. View and re-open recently closed tabs. 🍪 Cookie Editor Full cookie manager for the current site. View, edit, add, and delete cookies. Export cookies as JSON. Expand any cookie to see and modify all fields including domain, path, SameSite, secure, and httpOnly flags. 🔀 Redirect Tracer See every redirect hop your browser took to reach the current page. Shows status codes (301, 302, 307, etc.) with a visual chain. Copy the full redirect chain to clipboard. 🌙 Dark Mode Instant dark mode for any website using CSS filter inversion. Adjustable brightness. Toggle per-site or globally. Images and videos are automatically re-inverted so they look normal. 𝕏 X Dim Mode Custom dim theme for X/Twitter with 7 color palettes: Dim, Slate, Jade, Plum, Dusk, Ember, or a custom hue. Live preview in the popup. ⚡ JS Toggle Disable JavaScript per-site with one click. Useful for debugging, reading articles without popups, or testing progressive enhancement. Page reloads automatically. 🚫 GDPR Cookie Consent Dismisser Auto-hides and auto-clicks cookie consent banners. Supports OneTrust, CookieBot, Didomi, Quantcast, GDPR plugins, and dozens more frameworks. Toggle off if a site breaks. 🎨 Live CSS Editor Write custom CSS for any website, applied in real-time as you type. Saved per-domain. Supports tab key for indentation. 📺 YouTube Unhook Removes YouTube distractions: no homepage feed, no sidebar suggestions, no end screen overlays, no Shorts. Search still works — just no algorithmic recommendations. 🎵 Music Recognizer Shazam-like music identification for any tab. Captures 10 seconds of audio and identifies the song via ACRCloud (free signup, bring your own API key). Results link to YouTube. History of recognized songs. 🖼 Picture-in-Picture Pop the largest video on the current tab into a floating PiP window with one click. 🗺 Google Maps Links Re-adds clickable Maps links and map preview cards to Google Search results. 🖼 View Image Adds a "View Image" button back to Google Images, linking directly to the full-size original image. {} JSON Formatter Auto-detects pure JSON response pages and formats them with syntax highlighting, collapsible sections, and a dark theme. Copy or view raw with one click. Never triggers on regular HTML pages.show more

This is my "feel the AGI" moment: I used... GPT-5.6 Sol to train my own autocorrect model that outperforms GPT-5.6 Sol (wtf??) I have no ML background. I have no idea what I'm doing. I just kept pushing Sol until it spat out a SOTA model. And I spent $0. The motivation: Years of talking to AI have made me terrible at typing. Rather than fix my skill issue, I decided to throw more AI at it. My idea was: instead of autocorrect that interrupts my flow, I want to type fast with mistakes and have AI clean it up after. I wanted the smallest local model possible, for speed, for battery life, for science! So I decided to train my own. Inspired by Andrej Karpathy’s autoresearch, I ran Codex /goal with this setup: pick an experiment, try it, record the results to a doc, throw it out if it fails, and plan the next experiment without repeating failures. I gave a few examples that had to pass, tight latency targets, and let it run. Sol did some amazing things. First, it scanned benchmarks and shortlisted base models: Qwen 3.5, Gemma 4, Liquid LFM 2.5. It found a dataset on HuggingFace for typed text. Then it built a simulator for fingers striking a Mac keyboard, modeling the physical layout with a Gaussian distribution around each key. It simulated striking the wrong key, wrong order, fat-fingering, etc. With the models + data + simulator, it fine-tuned using MLX right on my MacBook. It had a working prototype within an hour! But accuracy was pretty poor. — Problem 1: Tokenization Sol read papers, ran tests, and identified that the tokenizer was the bottleneck. Tokenization makes typos hard for the model to see, so it memorizes mappings instead of using its language priors. Sol tried ByT5, Google’s tokenizer-free byte-level LLM. This made a big improvement, but the model is old and lacked the knowledge needed to reach Sol performance. Sol dug deeper and realized a tokenizer-free model isn’t needed; instead, it used T5Gemma, an encoder-decoder model. This can understand the input deeply before producing output, and furthermore, Sol could post-train the encoder to improve performance. This gave a much higher ceiling. — Problem 2: Loss function Now the model was correcting some typos perfectly, but ignoring most. Sol realized that standard cross-entropy loss was teaching the model to avoid edits, because the vast majority of characters in the training data were left unmodified. The fix was wild: Sol wrote a custom loss function that byte-aligns the source and target strings, uses a dynamic programming algorithm to compute the minimum edits between the two, then weights correct edits much higher than copies. After a lot of tuning, this dramatically improved accuracy. — Problem 3: Autoregression One failure mode remained: if the model made a mistake, it couldn’t backtrack. It could only predict the next token. Teaching it to “think” like a reasoning model would solve this, but would be far too slow. Sol found a beautiful solution: instead of greedily predicting the next token, beam search over all possibilities. This parallelizes the exploration instead of one linear chain-of-thought. At the end, choose the path with highest cumulative log probability. This worked great, but made the experience worse, since the user wouldn’t see progress until the whole search was done. To fix this, Sol made a clever observation: after each search step, the longest common prefix among surviving branches is guaranteed to appear in the final result, so it can be displayed immediately. As the search progresses, weaker paths are dropped and the prefix grows, so the user sees continuous progress. Sol built all this as a custom MLX pipeline that does the parallel decoding on the MacBook GPU, with just ~40ms TTFT. It’s crazy fast and entirely local. — Final eval (error reduction rate, higher is better): - Apple autocorrect: 49.66% - GPT-5.6 Luna: 82.47% - GPT-5.6 Terra: 87.64% - GPT-5.6 Sol: 90.56% - Our model (1.7B): 91.02% Final cost: - 1 quota reset (thanks Tibo) - $0 (And yes, I verified there's no cheating. In fact, we test words scrubbed from the training data to prove the model isn’t memorizing) There were a ton more details and tangents I could write about: contrastive learning, GRPO, DPO, dynamic masking, and more. Sol is a fascinating and creative model. It blew my mind so many times. Don’t let a lack of experience stop you: Sol makes AI experiments accessible to anyone!show more

Two years ago one guy won $579K on sports... betting and wrote a detailed thread about how he did it. His model was completely public: formulas entry logic match selection criteria. Hundreds of people copied the approach and started trading using his system. 3 months later the edge completely disappeared bookmakers adapted lines started moving faster windows closed. Everyone who copied lost their deposits. He himself hasn't shown his profile to anyone since and doesn't write publicly about his bets. This is a standard story in the world of sports betting: any advantage dies the moment the crowd finds out about it. Yesterday I found a wallet that contradicts everything I know about this market. > $4M+ in pure profit in 14 days. This wallet doesn't know how to lose: The profile is completely public every position every entry every exit visible to anyone with internet. He absolutely doesn't care that you're watching. The first thing that catches your eye when you open the stats the numbers just don't match reality. The most profitable sports models in the world the ones that feed professional syndicates in Vegas give 54-57% accuracy. That's the ceiling. That's the edge. That's enough to live off betting for decades. > This wallet has 1207 closed positions in two weeks of trading. I scrolled through every one. Won. Won. Won. Won. Won. Scroll further. Won. Won. Won. Won. All the way to the end of the list. > Zero losses. Not 90%. Not 99%. Zero. This is not betting in the sense we understand the word betting by definition assumes you sometimes lose. Then what am I even looking at? I open his largest closed trade to understand the scale. > Bills vs. Jaguars NFL playoff final. > Entry: $1.13M. Exit: $2.45M+. > Pure profit of $1.32M+ from one football game. And the most interesting part this wasn't even a simple bet on the winner where you just need to guess who wins. This was point spread: Bills -3.5. To take the money he needed Bills to not just win but win by at least four points. Not guess the match outcome guess by exactly what margin the game ends. And he bet over a million dollars on it. I scroll through the rest of the closed positions and see the same pattern. > Packers vs. Bears invested $781K took $1.79M+ pure profit over a million. > Stade Rennais FC 1901 invested $602K took $1.56M+ pure profit $964K. > Patriots spread -3.5 invested $600K took $1.27M+ pure profit $675K. > Rams spread -3.5 invested $613K took $1.25M pure profit $641K. Every position hundreds of thousands of dollars on the line. Every one closed in profit. I start looking for any pattern in his match selection trying to understand the logic. > NFL American football. > NBA basketball. > NHL hockey. > Premier League English football. > Bundesliga German football. > La Liga Spanish football. > Ligue 1 French football. > Serie A Italian football. He doesn't focus on one league and doesn't specialize in one sport. This completely kills the insider information theory it's physically impossible to have reliable sources simultaneously in all professional leagues in the world. I look at the timing of his entries when exactly he opens positions relative to match start. > Average entry price in his positions: 35-50 cents per share. > Price of the same positions at match start: 70-85 cents. He consistently enters when the price is still low hours sometimes a day before the main mass of players starts loading money in the same direction. Two possible explanations. 1. He somehow knows match results in advance. 2. He sees something in publicly available data that hundreds of thousands of other people looking at the same matches don't see. I can't think of a third option. > The fattest win in the wallet's history: $1.32M+ pure profit from one position. That's more than most people will earn in their entire lives. He did it in three hours while a football game was on. I look at current open positions he didn't stop and didn't withdraw the money. > Right now he has $4.2 million in active bets. And all of them are already in profit even before closing. > Spread: Indiana -7.5 entered at 50 cents now price 100 cents profit +100%. > Sharks vs. Lightning entered at 73 cents now price 100 cents profit +37%. > Wild vs. Maple Leafs entered at 50 cents now price 100 cents profit +100%. > Tottenham win entered at 35 cents now price 100 cents profit +185%. He didn't just not stop he's accelerating and increasing position size every day. > 64 thousand people are already watching this profile right now. People are trying to copy his trades in real time. They can't keep up. By the time a new position appears in the public profile the price is already completely different because his entry itself moves the market. He doesn't hide and doesn't conceal trades because speed is his protection from copying.show more

Why is the market selling off today? (Save this).... Today's selloff is bigger and messier than what we've seen lately, KOSPI crashed almost 11% overnight, chip stocks are getting hit everywhere and it's not because AI demand suddenly disappeared but rather a bunch of fears piling up at once that I think are getting way overplayed. Start with the AI ROI thing since it's been building since last week's earnings. Tesla and Alphabet both kicked off earnings season with big capex numbers and negative free cash flow and even with strong revenue growth both stocks got hammered. That set the tone of we don't care if capex is growing, show us the cash, and it's carrying into this week with Amazon, Meta, Microsoft and Apple all reporting, which isn't helping the nerves. But look at what actually happened with Alphabet, cloud revenue grew 81%, total sales grew 24%, that's not a company torching cash on nothing, that's a company scaling into demand it can barely keep up with. Negative free cash flow during a capex supercycle is normal, you build the data centers and buy the GPUs before the revenue shows up. Judging a buildout phase like it's a mature business is the wrong lens, and that's basically what happened last week and what's still happening today. Then there's China chip competition, which is honestly the biggest accelerant of today's move. CXMT's IPO shares rose over 466% and combined with headlines about China's homegrown DUV lithography progress, it triggered a brutal rout in Korean chipmakers, Samsung fell as much as 13%, SK Hynix over 14%, Kioxia nearly 18%, dragging the KOSPI down almost 11% and into an eighth circuit breaker this year. That spilled straight into Nvidia, ASML, Sandisk and Seagate here in the US, with Nasdaq 100 futures down over 1% before the bell. But here's the thing, five DUV units this year against ASML's 131 a year, running performance closer to a 2008 design, is not an equipment moat collapsing, it's a headline that's gotten repeated so much this week it's built its own gravity. These tools are aimed at mature nodes like automotive and industrial chips, not the leading edge logic or HBM that actually drives the AI trade, so the read through to Nvidia, ASML or Applied Materials earnings power is basically nothing. The CXMT pop is scarcity, people bidding up the only pure play China memory stock they can get their hands on, not a sign that oversupply is coming. And Korean chipmakers dropping 12 to 14% in one session looks a lot more like leverage unwinding after a parabolic run than a real rethink of Samsung or SK Hynix's HBM backlog, which both companies have already said is basically sold out for the year. Geopolitics is actually the one spot where the news should be helping, not hurting. US and Iran hostilities seem to have paused for now, which should be easing oil driven inflation fears. If this were purely a geopolitical panic you'd expect oil spiking and yields following, but that's not what's happening, this move is chip specific and Asia led, not an oil shock like a week or two ago. Rates and the Fed are still in play, decision lands tomorrow, and people are nervous about higher for longer language even though a hike isn't the base case. On top of that, reports that Nvidia's five year credit default swap costs jumped by a record margin are getting read by some as a credit risk signal tied to all this AI debt spending. But a one day CDS spike during a market wide panic is a fear indicator, not proof of an actual credit problem, spreads on every big name widen fast when volatility spikes, Nvidia's balance sheet hasn't changed in the last 24 hours. Fed futures are pricing in essentially no chance of a surprise hike tomorrow, this is a hold meeting, and I'd bet the hawkish jitters fade fast once Warsh actually talks. Then there's the bigger liquidity and positioning story, which I think explains more of today's violence than any single headline. KOSPI is down nearly 29% for the month now, steeper than 2008, mostly because Korean chipmakers had turned into crypto like gambling tokens, running way too far, too fast on retail leverage and margin debt, and now unwinding just as hard on the way down. That's positioning excess getting flushed, not HBM demand disappearing or hyperscalers pulling back. Nothing in the actual order books, capex guidance or HBM contract pricing has changed, DRAM and NAND prices are still climbing quarter over quarter, nobody's canceled a GPU order or a data center project. What changed is how much leverage was sitting on top of this trade, and that's getting ripped out in one ugly session. This is one of the scariest looking selloffs we've had all year but scary looking and actually broken are two different things. Every headline driving today, the China lithography story, the CXMT IPO, the Nvidia CDS spike, the Fed jitters, looks a lot less scary once you dig into the actual numbers, and none of it touches real AI infrastructure demand or supply. This looks like leverage and sentiment unwinding, not the long term thesis breaking. If you want to see exactly what I'm buying into this, join Milk Road Pro for just $1 using the link below.show more

For two years I have been living in a... kind of parallel universe called GME.Every single day, every week, every month I have read everything. Every filing, every tweet, every SEC filing, every forum post, every leak. I stared at the charts until my eyes hurt. And over and over the same certainty kept rising: The shorts never closed. Never. They only found new tricks, new derivatives, new lies, new media campaigns but the position? It is still sitting there. Like an elephant in the room that everyone pretends is just a piece of furniture.I was never wrong.I was just damn early.For two years I watched people call me a Hopium Junkie, friends rolling their eyes, family asking if I didn’t want to give up eventually. Two years in which the price sometimes dropped so low that even I doubted for a moment not the thesis, but only whether I could still endure it all. But deep down I knew: I am not wrong. I am simply the guy who saw the first cracks in the wall years before the earthquake even started.And now?Now the GameStop business has turned 180 degrees.The company that was once mocked as a dying dinosaur has reinvented itself. Cash flow positive, debt gone, new strategy, new leadership, new vision. No more desperate “we are trying to survive somehow” but a clear “we are building something here that none of you saw coming.” The revenue numbers, the balance sheet, all the moves everything suddenly screams: They didn’t just survive. They have transformed.And as if that weren’t enough, Michael Burry is back in the game too. The man from The Big Short, who was already in GME early, is buying again. He is long and adding. Burry trusts Ryan Cohen to become the next Warren Buffett. He sees in him a young, driven CEO with the right mindset who can create something truly special.GameStop is sitting on almost 9 billion dollars in cash and marketable securities. And Ryan Cohen is planning something big, something that has never happened before in financial history. He talks about a transformational acquisition in the consumer or retail world, about brutally efficient execution, about a model similar to Berkshire Hathaway but much faster. A deal that could take the company from a 10 billion market cap to something worth a hundred billion or more.And I sit here, after two years, with this feeling in my chest that I can barely put into is not satisfaction. Not yet. It is this deep, quiet “I told you so” mixed with an incredible calm. Because I know the big moment is still coming. The shorts are still there. The bill is still open. And this time the company is no longer the old GameStop that they almost tore apart in 2021.This time it is a completely different beast.And me? I am still here. Not because I am stubborn.But because I was right from the very beginning.Only time has finally caught up.While others who have been in this for five years or more never gave up. They held through every dip, every FUD wave, every moment of doubt, and they are still standing right beside me. True diamond hands. Their patience and conviction have been unbreakable, and now we are all watching the same story unfold together. $GME 🏴‍☠️🐇🍦🐸🍻 741676935420show more