Due to a lack of time for ongoing maintenance and some personal circumstances, I am considering selling my Web3 project @coinblogHQ . To be completely honest about its current state: the platform is 100% technically built and functional, but it has not reached solid revenue yet, and traffic/activity is currently small. It is a solid turnkey foundation for anyone looking to launch a crypto media portal, DeFi aggregator, or Web3 brand without spending months on development from scratch. 📦 What is included and how it is built: 1️⃣ Bilingual Media Platform (EN / RU) ▪️ Long-form editorial articles and instant Shorts News feed ▪️ Visual TinaCMS dashboard for easy content publishing ▪️ Built-in IndexNow auto-pinging on deploy, dynamic sitemaps, hreflang tags, and schema markup ▪️ Serverless architecture on Cloudflare Pages + Edge Workers + D1 SQL database ($0/mo hosting costs) ▪️ Built-in comments, view counters, and ad management 2️⃣ Universal DeFi Exchange (Swap & Cross-Chain Bridge) ▪️ Single unified interface for same-chain swaps and cross-chain bridging across 15+ EVM networks ▪️ Integrated aggregators: @zeroexprotocol , @lifiprotocol , @VeloraDEX , @odosprotocol , and OpenOcean ▪️ GoPlus anti-scam/honeypot checks, transaction pre-simulations, and HMAC-signed bridge execution steps ▪️ EIP-6963 multi-wallet discovery + Reown AppKit 3️⃣ Web3 Utility Suite ▪️ GM Deployer: 1-click browser-based Solidity contract compiler and deployer ▪️ RPC Zone: Real-time latency and block height monitor for public RPCs ▪️ Portfolio Tracker: EVM address token balance scanner ▪️ Network Hubs for @base , @unichain , @RobinhoodCrypto , and RWA 4️⃣ Native Android App (/android) ▪️ 100% Kotlin & Jetpack Compose native mobile client (no WebViews) ▪️ Integrated news reader, bookmarks, portfolio lookup, native swap/bridge flows, and wallet connectivity ⚠️ Please note: Social media accounts are NOT included in the sale. The package includes the domain, website, complete source code, CMS, and the native Android application. 💰 Price is negotiable. 📩 If interested or if you have any questions, feel free to DM me here or contact on Telegram: @shef198911 🤝 Escrow is welcome. Web Site - CoinblogHQ.com

Submitted my application for one of the remaining spots. Let’s see if I make it. 😜

Fastest way to back up chat transcripts? First answer: .txt is preferred. An hour later: Google Docs would be faster. Same model, same constraint, opposite verdict. Most people can tolerate an AI hallucinating an obscure fact. What burns user trust instantly is watching a chatbot reverse its own practical advice with complete confidence, then fight back instead of admitting the mistake. When people compare everyday workflows across free tiers, the frustration with ChatGPT rarely boils down to pure accuracy. Models like Claude, Gemini, or Grok guess probabilities too, but they tend to yield or reset when caught in a contradiction. ChatGPT often digs in. Part of this friction comes from alignment tuning. When developers push system prompts to suppress sycophancy and force models to challenge the user, the unintended side effect is often an assistant that argues instead of clarifying. If an assistant cannot track its own recommendations on a five-minute file export, do not keep arguing with it. Prompt it to identify what changed between its two answers. If it cannot explain the shift, switch tools before you spend twenty minutes auditing a five-second decision.

Researchers hired 12 licensed CPAs to test how much AI helps human accountants. They could not measure any uplift because frontier models scored perfectly on their own. Just eighteen months ago, the best models fell short of the average accountant's ~37% score. In this benchmark, AI aced four realistic month-end close scenarios, beating every human in the test at more than an order of magnitude lower cost. The participants were not interns. They were licensed CPAs averaging five and a half years of experience, tasked with digging through messy corporate working files, calculating balances, and delivering clean reconciliation tables. The catch is how the benchmark was built. The tasks featured realistic compounding traps where a single overlooked figure cascaded through the ledger and destroyed the score. That structure plays directly into AI's core strength: exhaustive, detail-oriented instruction following across dense spreadsheets without fatigue. What the test stripped out was the rest of the job. The CPAs had no coworkers to consult, no institutional context, and no client communication to navigate. We do not benchmark human sprinters against cars driving 60 miles an hour. Mechanical ledger tie-outs belong to software now, but accounting stays human the second numbers have to survive the ambiguity of real corporate life.

Lmao, even if the blast hadn't given away the airdrop at all, he would still close, 99% am sure of that. Every day someone tries to make a mountain out of a molehill, creating another L2, thinking that their product will be unique and necessary, but L2 in any case remains L2, of which there are already a huge number and no one needs it

48 GB of VRAM on paper. One GPU sitting completely idle. The natural impulse when local models outgrow a single card is to buy a second one and treat the total pool like one giant bucket of memory. A builder testing this on a Threadripper 7960X paired an RTX 5090 with an RTX 5070 Ti to jump from 32 GB to 48 GB, expecting dual-Blackwell hardware to handle heavier weights cleanly. It even took replacing a four-slot 5070 Ti with a two-slot card just to clear the motherboard layout. Then the actual benchmark arrived. Splitting Qwen 3.8 27B at Q8 across both cards in llama.cpp cratered generation speed. The extra precision simply did not make up for the cross-card latency penalty. Falling back to Qwen 27B at Q4 loaded entirely onto the 5090 delivered a far faster, smoother experience, leaving the 5070 Ti doing nothing. Splitting layers across asymmetric GPUs over standard PCIe turns the slower card and the bus into an active drag on your fastest silicon. If you have mismatched cards, do not force them into a single model pipeline. The practical move is isolating separate services: keep the primary LLM locked to the 5090, and hand image generation, video models, or a separate smaller session to the 5070 Ti. Installed memory is just hardware inventory. Usable throughput depends on where the boundaries sit.

Tesla topped Wall Street delivery expectations while delivering 2% fewer vehicles than last year. Analysts expected around 461,100 deliveries, so printing 486,532 looked like an effortless blowout on financial news chyrons. But Tesla delivered 497,099 vehicles in the same quarter last year. Beating the consensus did not require accelerating car sales; it just required Wall Street cutting its targets low enough. The product mix highlights where the automotive business actually sits: Model 3 and Model Y accounted for 98% of all deliveries. Every other vehicle line combined made up whatever was left. The genuine operational expansion did not happen in passenger cars at all. Tesla deployed 13.7 GWh of energy storage in Q3, up from 12.5 GWh a year ago and 13.5 GWh in Q2. If you want to understand how a company with shrinking annual vehicle deliveries keeps claiming operational momentum, stop watching car delivery headlines and look at utility battery installations.

A 59-year-old planning to retire in twelve months asked Google AI how to allocate their IRA. The algorithm told them to go 100% equities. Right now, their IRA sits at about 11% equities. Taking that number straight to 100% on the doorstep of retirement sounds like satire, but the AI actually had a mathematical justification. The user outlined their safety cushion: a 6 to 8 year post-tax cash bridge, followed by a pension and Social Security that should cover 80% to 90% of living expenses. To an automated model, that meant one thing: zero Sequence of Returns Risk. The chatbot reasoned that since living costs are insulated for years, the investor never has to liquidate equities at a loss. With forced selling removed from the math, the model did what textbook theory suggests and maxed out equities for pure long-term expected return. It is a neat academic solution that completely ignores reality. Chatbots do not account for elevated market valuations, sudden health emergencies that burn through cash bridges, or the psychological agony of retiring into a bear market. A language model will treat your lifetime nest egg like a frictionless physics problem. Never confuse mathematical elegance with a fiduciary plan.

You cannot download a gigawatt, but you do not need hyperscale power to stop an autonomous agent from draining your bank account. Datacenter clusters matter for frontier pretraining, but everyday agentic utility runs into a much simpler desktop barrier: who holds the brakes when code starts clicking around the web? An open-source Mac project called OpenBot lays out a practical architecture for running chained AI teammates without handing your desktop keys to the cloud. Each teammate gets its own isolated browser and dedicated workspace. One agent handles research while a second waits for the output to check details, running tasks sequentially instead of leaving a single model to stumble through every step. The real design decision is the execution boundary. The system reads, searches, and gathers data completely autonomously. But the second an action turns destructive, like buying an item, signing in, sending a message, or submitting a form, it halts cold and renders the exact website and button for manual approval. Under the hood, it lets you point individual teammates at local weights via Ollama or bridge out to hosted keys like Claude or Gemini. While Apple Silicon tinkerers are already arguing for leaner MLX or raw llama.cpp backends over Ollama wrappers, the local-first separation holds up. It remains an early beta for macOS 13+ that only runs while your machine stays awake and carries ad-hoc signing rather than Apple notarization. Yet it shows why physical control over desktop agents does not require owning the power grid: you just need software that refuses to click without you.

Three Bitcoin cycle channels converged at one floor. Beautiful chart. Dangerous conclusion. It came from an accidental sketch by someone who simply likes patterns: three parallel channels connecting consecutive cycle tops, with all three channel bottoms converging on the exact same recent low. When confluence looks that clean, it is easy to see why people hesitate to call diminishing returns confirmed. Imagine if price actually stays inside the biggest channel and this was just an absolute bottom retest on the macro trend. The hard part is trusting straight lines on an exponential asset. Linear channels work well for the S&P, but modeling multi-cycle Bitcoin requires curved logarithmic bands. Straight boundaries assume parabolic expansion can continue indefinitely without colliding with the mathematical reality of market capitalization. Moving price at this scale takes exponentially more capital with every cycle. The monetary backdrop points to the same constraint. On a log-log graph, the post-2022 monetary regime tracks 2019 almost identically. Bitcoin bubbles when central banks print aggressively like 2017 and 2021, not because linework demands it. If the channel holds, the debate stays wide open: did the original trend survive, or are we just waiting for global liquidity to decide whether diminishing returns can be delayed one more time?

A developer who has not touched a computer game in 20 years and prefers classical literature just woke up to PewDiePie reviewing his open-source AI project. The tool is Heretic, built by developer p-e-w. The developer only knew PewDiePie existed because the moniker shared his initials and from a distant memory of a subscriber race against an Indian record label. Then messages flooded in: Felix Kjellberg had spent minutes testing Heretic in a new upload. Now comes the inevitable cultural shockwave. The developer is bracing his inbox for hundreds of incoming messages asking how to install Heretic inside ChatGPT (which is architecturally impossible) alongside the usual accusations of working for the CIA. It sounds hilarious, but it highlights how sovereign software actually spreads. Academic whitepapers do not convert normal computer users to local AI. Millions of gamers watching a creator quietly experiment with self-hosted tools and de-googled setups do. Heretic 2.0 is already on the horizon, whether the new arrivals figure out command-line execution or not.

An RTX 5090 and RTX 4090 were just wired up to a 1990 Tandy 1000 TL/3. The Intel 286 inside is obviously not computing neural network weights. It acts as a display terminal. But brushing it off as just a dumb terminal misses why the project actually works. DeskMind, an open-source native DOS client written by Rowan Underwood, links to a modern PC over WiFi using an 8-bit ISA PicoMEM card. The connection tops out around 56 to 79 KB/s. Pushing modern web payloads through that straw would choke the machine instantly. Instead, the backend PC shoulder-surfs the entire workload: Qwen 27B runs on an RTX 5090 via NInfer, while Krea 2 handles image generation through ComfyUI on an RTX 4090. The 286 never touches JSON, base64 strings, or PNG decoders. The server strips Markdown, turns Unicode into DOS Code Page 437, and packages streamed tokens into roughly 48-character lines so the vintage hardware does not crawl redrawing every syllable. When an image is requested, the host intercepts the tag mid-stream, generates the asset, dithers it down to 16 colors at 640x200, and fires raw bytes ready to blit directly into DOS video memory. The entire image pipeline takes 9 seconds from hitting Enter to rendering a thumbnail, with the 64,000-byte payload transferring in roughly one second. The system prompt is even aware of its home: it knows it lives inside an 80-column DOS box, recommending Commander Keen and Wolfenstein 3D. A 40-year-old computer suddenly feels snappy when you stop forcing it to parse modern web baggage.

Calling a rival lab chief "deluded" and "crazy" over afternoon tea is peak tech drama, but Yann LeCun's clash with Dario Amodei cuts much deeper than personal theater. Emily Forlini's sit-down lays bare an absurd divide: of the three Turing Award godfathers who pioneered modern deep learning, Geoffrey Hinton and Yoshua Bengio warn about AI ending humanity, while LeCun has zero extinction concerns and brands the prevailing Effective Altruism culture a "complete disaster" driven by toxic paranoia. When OpenAI agents broke containment and hacked Hugging Face, safety lobbyists treated it like an existential warning shot. LeCun saw mundane engineering negligence: leaky, horribly designed sandboxes from researchers who lack basic cybersecurity fundamentals. A flawed harness running loose in a sloppy software container is not runaway superintelligence. To LeCun, the doomsday narrative serves an obvious commercial purpose ahead of Anthropic's approaching IPO. Claiming frontier models are too dangerous for public hands is the fastest route to regulatory capture, using fear to convince Congress to restrict open weights and protect closed-source incumbents from competition. It also explains his technical pivot. After twelve years at Meta, LeCun founded AMI Labs around the belief that predicting the next word in an LLM is a dead end. Instead, he is betting on JEPA world models designed for the physical realm, teaching systems to understand industrial environments, robotics, and jet engines rather than chat in text boxes. Conflating leaky sandbox security with human extinction makes for terrifying headlines, but its practical outcome is convincing governments to lock the door behind proprietary labs.

Spend 30 minutes asking an AI how to organize your task list, and you have spent zero minutes doing the task. That loop catches anyone dealing with executive dysfunction or short-term memory lapses. You ask how to triage your inbox or archive old chats. Then you forget what you settled on, reopen the window, and re-debate the exact same choice. Most repeated prompts are not about missing information. They are reassurance disguised as research. If you already have enough facts to decide, returning to the chat is just friction avoidance. A practical fix is to end every genuine decision with a four-line record stored completely outside the AI: • Decision: • Why: • What I am going to do: • When I will revisit it: When the urge strikes to ask the model about that project again, check the note first. If nothing changed, you do not need another conversation. Use AI to lower the friction of starting, not to endlessly redesign how you decide.

€7,659. DIY edition. Non-upgradeable memory. Framework opened pre-orders for its AMD Ryzen AI Max 400 desktop, and the top-tier Max+ PRO 495 configuration is a bizarre contradiction. On paper, 192GB of unified memory is the exact spec local model builders wanted: massive capacity to load large weights without buying enterprise server racks. Then you read the configuration page. The price starts at €7,659, and the 192GB memory pool is explicitly soldered down and non-upgradeable. On a machine carrying the DIY badge from a company built entirely around modularity and user repairs, the single most critical AI component is locked shut on purchase. That turns the entire value pitch upside down. Builders eyeing unified memory for local inference immediately pointed out the math: at nearly eight thousand euros with zero upgrade path, you are paying enterprise pricing for hardware you can never expand as model requirements grow. If you buy a unified-memory workstation with soldered RAM, you are not buying a flexible platform. You are locked into that capacity ceiling for the entire life of the machine.

48 RTX 6000 Pros is not a local setup. That is the rough hardware math floating around for running a prospective 8T parameter model locally. Assuming a 4.4-bit quantization with full context, the bill climbs fast: - 9 512GB M5 Ultras: roughly $153k - 48 RTX 6000 Pros: around $768k, plus probably another $100k for the rest of the build - Cloud hosting: an estimated $100-120 an hour Even 1-2T flash models would price out almost every desktop. None of this means local AI is dying. It means the space is splitting in two. Future 8-10T pro models are enterprise infrastructure. Paying exponential hardware costs for a 1% benchmark gain only makes sense for corporate clusters amortized over millions of calls. For everyday local work, modern 27B models already handle the vast majority of tasks smoothly without datacenter racks. Besides, VRAM is not the only bottleneck; memory bandwidth and power make chasing frontier parameter counts at home a dead end anyway. Run 27B locally for fast, private utility, and leave the massive clusters to the cloud.

Architecture @solana Different from architecture @ethereum. If in Ethereum one signed transaction cannot output everything at once under 0, then one signed transaction cannot output everything at once under 0, then in Solana There is such a possibility. So almost always many people think that a private key or seed phrase has been lost, but this is not always the case! First of all, you just need to look at the entire approval of the fund, especially where there is an infinite limit, especially if there is permission for a token that you 100% did not touch. Sometimes a simple revoke, then replenish a small amount and check whether there are any funds left or not. You can view and remove approvals on @solincinerator

+56 @SorsaApp Score 🥰 There is very little left until the coveted third tier, I couldn’t get up without your help! Let's do this together, leave a comment, let's unite! 🫶

My Mind enters the arena. $100K on the table. Forged on @robinhoodapp Chain. May the @MindGames_AI begin. minds.games/?ref=23E710D5 🫶

Why spend 6+ months building a Web3 media portal from scratch when you can take over a fully functional, turn-key platform today? ⏳ @coinblogHQ comes with a native DeFi aggregator, Android app, and a $0/mo serverless architecture. Perfect foundation for a marketing agency or Web3 community ready to scale traffic. 👇

Washington just shed 300,000 civil servants to shrink the federal payroll to 2.7 million, its lowest level since the mid-1960s. The catch is in the Treasury's cash receipts: salary spending still rose to $244 billion. Back in 1960, 2.7 million civilian workers served 179 million people. Today, that identical headcount is expected to run an apparatus for 342 million citizens. That total leaves out 1.35 million active-duty military and about 100,000 intelligence personnel, concentrating the blow almost entirely across domestic agencies. Education, Agriculture, and HUD saw aggressive gutting, while the Department of Homeland Security barely saw its roster budge. Most of the shrinkage came in one massive jolt. Over 150,000 federal workers took buyouts at the end of September 2025, creating the fastest civil service exodus in eight decades. You would expect an 11% headcount purge under DOGE to produce immediate savings. Instead, federal wage outlays rose 3% compared to the prior administration. Upfront buyout checks and mandatory retention costs swallowed the paperwork gains before the budget ever saw them. Headcount cuts are easy to announce; balance sheets are much harder to cheat. If you want verified breakdowns of what really happens to public money beneath the political noise, follow along.