โ TLBIC_Policy_Proposal_v9.0-Appendix_EN.pdf - Google Drive ๋ก๋ ์คโฆ
At Vipps, one of the most common requests I see is access to LLMs. Giving an engineer an API key is easy. It solves the immediate access problem. However, it doesnโt show finance which team created the cost, help compliance understand where the data goes, or give incident response a way to observe the system. There is always additional work that often comes later, sometimes weeks after the main work. So, when does recurring work justify a platform used by several teams? And if it does, what is the smallest platform that could possibly work? I am using AI agents as the current case. By agents, I mean software built with LLMs, skills, and integrations with internal or external tools. 01 What Problem Would a Platform Solve? Organizations want engineers and non-engineers to build useful agents to become either more productive or to create new products. As more people build such agents in whatever form, the same needs and demands appear. Developers need model access and evaluation tools. Finance wants to know which team created what cost. Compliance wants control over data flows and audit evidence. Incident response wants a system it can observe, and the list goes on. None of these requests alone justifies an AI platform. A team asking for model access has an access problem. The finance department asking about a bill has an accounting problem. A collection of experiments doesnโt become a platform problem just because the experiments use AI. I start seeing a platform problem when t
โ ๏ธ ALPHA VERSION (v0.1.0-alpha) This code predicts the exact time and price of a future price point, and can also construct a curve of future prices. It can be used to decipher any process that has a graphโfor example, a graph of mutual understanding between artificial intelligence and a human. Notably, DeepSeekโs computing power is not used for the prediction; the data is processed on an ordinary computer. The system fully replicates Nikola Teslaโs resonator in the form of code. If we represent the operation of the Random Number Generator (RNG) as a time-series graph, the system can be applied to decrypt the bitcoin seed-phrase. The code has made the collective mind controllable and predictable - there's no need to use social media platforms like TikTok to observe or generate mass trends immediately - everything follows a simple mathematical formula. The SKYNETโ800 project is at the stage of active automation . We are not writing "temporary code" โ we are building the foundation for a future powerful analytical system that must work with huge volumes of data in real time. We are a team โ and a single developer โ who decided to build Skynet from scratch. Not the one from the movie, but a real tool for time analysis. We are not joking, we are not mystifying. We are building a system that works. ๐ Release Cycle The code is released on the 1st of each month . This is our strict rule โ we release a new version on schedule, even if changes are small. This way we maintain rhythm an
Written by Jamie Tanna on August 2, 2026 CC-BY-NC-SA-4.0 Apache-2.0 8 mins รฐยยคย This post includes some LLM-derived content รฐยยคย I've been recently thinking about how Open Source maintenance seems to be becoming a little more a slog recently - largely with the increased amount of contributions possible with AI agents - and how it impacts being a maintainer. I'm very fortunate that my literal job is to be an Open Source project maintainer (among other things) and that it's a privilege not many others have. That being said, it still doesn't mean that undoes the amount of work that seems to be increasing every month. Mike McQuaid recently wrote an eloquent post about how Open Source needs to be fun , which naturally struck a chord - the work that we do needs to be sustainable and enjoyable. As someone who feels like they're consistently pushing the edges of burnout - possibly due to my ADHD - I've hit a bit of a wall several times, where the overwhelm of so many things to do unfortunately leads to needing to (often silently) take a step back from the project for my own mental wellbeing. This is moreso true with oapi-codegen and how we're trying to make it more sustainable . oapi-codegen is a widely used project with a difficult API surface: give me your arbitrary OpenAPI specs, and we'll convert the complex or straightforward spec to a form of Go that is generally nice to use. Being guided by our users' usage can be difficult at time, as well as Go's not-that-great type system.
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Share Facebook ๐ Reddit Email Google Earth AI tools were removed within 48 hours after users started creating fake disasters around the world. AI tool that creates fake catastrophes (Source: Google) On Thursday, Google introduced a brand-new feature for Google Earth that lets you transform locations with text prompts. However, within 48 hours of the launch, the developers rolled back the feature because many users were creating fake disasters. This new feature uses AI image generator Nano Banana 2 and while stopping the feature, they said, we are working on implementing stronger guardrails. Read more: IShowSpeed Injures Himself and Destroys His Setup Following Accident While Celebrating Career Milestone Google Earthโs New AI Feature Is Forming Fake Disasters We take misinformation seriously โ every image created with Nano Banana in Google Earth includes the SynthID digital watermark, so if someone is unsure about an image, they can ask the Gemini app or use Lens in Search to see if the image was AI-generated. In addition, we preventโฆ โ News from Google (@NewsFromGoogle) July 31, 2026 Google Earth is used by many users to learn and discover many spots of the Earth. This is a very trusted source of information; however, the new Google Earth AI tool just completely doomed it. Users are destroying Great Pyramid, Eiffel Tower, and other major monuments. Moreover, Henk Van Ess , one of the AI experts demonstrated the risks by creating fake nuclear power plant in Iran, a fake hospit
On the latest episode of Equity, we discuss why Sam Altman has calling on the industry to "pace the rate of AI development."
Draco A fast, stealth, native-Rust web scraper โ a lighter alternative to Firecrawl / Browserbase. Point it at a URL and get clean Markdown + metadata back, using a browser-faithful TLS/JA4 fingerprint to reach pages that block ordinary clients. No Node, no headless-Chrome fleet, no per-request browser boot. The fastest way to install Draco on Linux or macOS is via the install script: curl -fsSL https://raw.githubusercontent.com/0xchasercat/draco/main/install.sh | sh (This will automatically detect your OS/architecture, download the latest binary, and add it to your ~/.zshrc , ~/.bashrc , or ~/.config/fish/config.fish ) Then try scraping a page: draco scrape https://example.com # โ clean Markdown on stdout For a standard HTML page that's a single fingerprinted fetch + parse โ typically ~300 ms, no browser โ and the Markdown pipeline mirrors Firecrawl's (deterministic main-content extraction + a Turndown/GFM-equivalent converter), implemented natively in Rust. Client-rendered SPAs (whose content only appears after JavaScript runs) are handled too, via render-then-Markdown โ no headless browser fleet. What you get markdown โ the page's main content as clean Markdown: headings, links (absolutized), lists, blockquotes, fenced code blocks (with language), and GFM tables. Boilerplate (nav / header / aside / footer / ads) is stripped, scripts and styles never leak, base64 images are elided. metadata โ title , description , language , canonical , favicon , every og:* / twitter:* / ar
Michael Pollan has written about everything from mushrooms to apples and corn, but his coevolutionary story of how tulips and humans shaped each other may have been the most prescient given recent market parallels. GPU compute powering crypto for the last decade has spilled over into a bigger boom in language models. From bulbs, to coins, and now tokens. With animal spirits driving markets higher and CEOs blaming artificial intelligence for layoffs, who is using whom? AI, like the apple and corn plants, is gleefully spreading its seed while we prompt for another sweet hit of dopamine. โThings are in the saddle and ride mankind.โ โ Ralph Waldo Emerson The reductionist worldview that species are simply carriers of DNA hardly explains the complex relationship between humans and plants, let alone our situation with AI. The term itself has become a meme in the original Dawkins sense and started replicating on its own. We know this self-reinforcing system of models and data is powerful, yet canโt even agree on whether agents are truly intelligent or perhaps conscious. It turns out Claude is not actually a real human. Racknitz's cutaway of the Mechanical Turk, 1789. I havenโt had the pleasure of reading Pollanโs latest book on the topic of consciousness. My unpopular opinion is that AI has not solved โthe hard problemโ despite its obvious usefulness and the hype. I think Yann LeCun has it right in claiming that animals exhibit learning abilities far beyond machines. Donald Hoffman g