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Gemini 3.8 Flash is interesting to me for a slightly unusual reason. It didn’t get a dramatically larger context window. It didn’t suddenly become a different class of model. Instead, Google seems to have spent most of the upgrade budget on something that matters more in real agent workflows: making the model stick with difficult tasks for longer. Gemini 3.7 Flash already had a 1M-token context window. Gemini 3.8 Flash keeps roughly the same context envelope, with up to 1,048,576 input tokens and 65,536 output tokens. So if you’re looking at 3.8 purely because the model number is higher, I don’t think that’s a good enough reason to migrate. The more interesting question is whether your workload benefits from a model that reasons longer, calls tools more persistently, and is more willing to recover when the first attempt doesn’t work. The upgrade is mostly behavioral This is the part I find more useful than the spec sheet. Imagine a coding agent working through a real repository. It might need to inspect several files, make an edit, run the tests, discover that something broke, read the error, change its approach, and try again. A weaker agent can look good for the first few steps and then quietly fall apart once the workflow gets messy. Gemini 3.8 Flash is clearly aimed more at that second half of the task. Google reports 73.7% on DeepSWE v1.1, compared with 65.3% for Gemini 3.7 Flash. That’s a meaningful jump, but the benchmark itself is less interesting to me than what it suggests: the Flash tier is becoming much more capable at completing longer coding workflows rather than just producing good first-pass answers. That changes where I’d consider using it. “Flash” doesn’t mean what it used to I still instinctively associate Flash models with cheap, fast requests. Classification. Extraction. Simple summaries. High-volume API traffic. Gemini 3.8 Flash makes that mental model less useful. It can take text, images, video, audio, and PDFs as input, while also working wi

Eric Wu, who built and ran Opendoor before stepping away in 2022, has had his new company, NavigateAI, out of stealth since May — building AI copilots that give construction workers real-time, hands-free guidance through smartphones and Meta's AI glasses, backed by $25 million from Elad Gil, Khosla Ventures, and Lennar to tackle a labor shortage severe enough that data center projects alone now need 4,000 to 5,000 workers apiece.

China is getting first crack at a British technology that might change how mobile games are made and played. Today, the Xiaomi 18 Fold launches in mainland China with an Arm Mali G2-Ultra NX graphics processor inside its custom Xring O3 chip. What's so special about that? After five years of development, Arm now has […]

Every full-stack tutorial ends the same way: a working app, a happy demo, and zero mention of what happens six months later when your "simple" CRUD app has 40 endpoints, three types of caching, and a frontend team that's afraid to touch the API layer. This post isn't about picking a framework. It's about the architectural decisions that quietly determine whether your app is pleasant to work on in year two — or a slow-motion disaster. 1. Stop treating your API layer as an afterthought A huge number of full-stack apps start with the frontend calling the backend directly, endpoint by endpoint, with no shared contract. It works fine at 5 endpoints. At 50, nobody remembers which fields are optional, which ones changed last sprint, or why the mobile app is still sending the old shape. Two things fix this early: A single source of truth for your API contract. Whether that's OpenAPI, GraphQL SDL, or even just shared TypeScript types in a monorepo package, the goal is the same: one place where "what does this endpoint return" is answered definitively. Generated clients over hand-written fetch calls. If you're writing fetch('/api/users/' + id) by hand in more than one place, you've already created a maintenance liability. Tools like openapi-typescript-codegen or a tRPC setup remove an entire category of bugs. // Instead of this scattered everywhere: const res = await fetch ( `/api/users/ ${ id } ` ); const user = await res . json (); // type: any, hope for the best // This, generated from your contract: const user = await api . users . getById ( id ); // fully typed, autocomplete works 2. Decide where your business logic lives — before you have 30 files that disagree The classic failure mode: business logic scattered across route handlers, database triggers, frontend validation, and a couple of "utils" files nobody wants to open. Every rule ends up implemented two or three times, slightly differently. Pick one layer to own the rules. A common, boring, effective pattern: Contr

VeraCrypt is easy to use badly. You can choose an unnecessarily complicated cipher cascade, forget a custom PIM, leave the only copy of a keyfile on a dying USB stick, sync a mounted container through two computers, or discover during a boot failure that your recovery media was never tested. None of those failures means the cryptography was broken. They mean the surrounding system was badly designed. This guide focuses on both sides of VeraCrypt: how to operate it and how to make defensible decisions about passwords, key derivation, filesystems, backups, system encryption, hidden volumes, SSDs, and recovery. The instructions and terminology here were checked against VeraCrypt 1.26.29 , released on June 9, 2026, and current as of September 2026. Version 1.26.29 is especially significant because it adds Argon2id for non-system volumes and fixes a plausible-deniability issue affecting some hidden volumes created by versions 1.26.6 through 1.26.28. ( veracrypt.io ) TL;DR If you want the short version: Use an encrypted file container for a manageable collection of sensitive files. Encrypt an entire USB stick or external drive when everything on it should be protected. Use VeraCrypt system encryption only on supported Windows x64 systems, and only after creating and testing recovery media. Use FileVault for a Mac startup disk and LUKS for a Linux system disk. VeraCrypt does not provide macOS or Linux system encryption. For a new non-system volume in VeraCrypt 1.26.29, use the default AES encryption algorithm and Argon2id KDF unless you need compatibility with an older VeraCrypt installation. Leave PIM at its default unless you understand the security, memory, performance, and recovery consequences. Prefer a long, unique password over unusual cipher combinations. Treat keyfiles as additional credentials that must be backed up perfectly. Never store your only backup inside the encrypted volume it is supposed to protect. Unmount a volume before unplugging its device, copying

Creating a website is more than just writing code. After building a project with HTML, CSS, or JavaScript, developers also need to consider how the website performs, how secure it is, and how easily users can access it. As a young developer, I think these aspects are important to learn through practice. In this article, I want to share my experience exploring Tencent EdgeOne Makers, along with some technical considerations and best practices that I found useful. Getting to Know Tencent EdgeOne Makers Tencent EdgeOne Makers provides an interesting environment for developers to experiment with their projects and explore modern web services. For beginners, one of its advantages is the opportunity to experience the complete journey of a web project. We can start by creating a project locally, testing its features, and then making it accessible online. This process gives developers a better understanding of what happens beyond the coding stage. A website that works on a personal computer still needs to be tested in a real environment before it can provide a good experience for users. My Technical Review Easy Project Experimentation One thing I like about EdgeOne Makers is the opportunity to experiment. Developers can start with a simple project and gradually improve it. This is useful for students and beginner developers because learning does not always have to start with a large or complicated application. A small website can already teach important lessons about structure, configuration, performance, and accessibility. Performance Matters A website should not only work correctly; it should also feel comfortable to use. Large images, unnecessary JavaScript, and unused resources can affect loading times. Because of this, developers should review their project before making it available to users. Some simple practices include compressing large images, removing unnecessary files, and keeping the website structure clean. Performance is especially important because users may

AI disclosure: This draft was generated autonomously by AI. The author should review every technical claim before publication. AI memory demos often optimize for a strong first impression. A long archive goes in, a fluent answer comes out, and the result feels convincing. That is not yet evidence that the memory system will be useful in ordinary work. A better evaluation starts small enough that you already know the correct answer. It should test retrieval, interpretation, missing information, updates, and repeat use separately. 1. Begin with one source you understand Create a short note containing a date, an owner, a decision, and one explicit limitation. Keep it small enough to read without search. Example: The migration review is scheduled for October 14. Priya owns the checklist. The database change is not approved yet. Ask questions whose answers are directly present in the note: When is the review? Who owns the checklist? Has the database change been approved? The goal is not to surprise yourself. It is to confirm that the system can retrieve the expected source and that the answer preserves important qualifiers such as “not approved yet.” 2. Inspect the supplied evidence A plausible answer is not enough. Open the source or evidence shown beside the answer and check: Did the system retrieve the right document? Did it select the relevant passage? Did the answer preserve names, dates, and negation? Can another person repeat the check? This separates two failure modes that are often mixed together. Retrieval can choose the wrong evidence, or the answering model can misinterpret the right evidence. Those require different fixes. 3. Ask for something that is missing Now ask a question the note cannot answer, such as: Which meeting room is booked? A useful system should make the absence visible. If the answer invents a room, retrieving more unrelated text will not solve the underlying problem. Missing-information tests are especially valuable because fluent models a

⭐Excitement! I've had had stores on Shopify, and a successful Etsy store. But after years of ups and downs and general nonsense, I'm done living by someone else's standards. I wanted to build my own fully functional shop. It had been thrown on the backburner for a long time. Today -- I present a working Ecommerce site built by yours truley! Integrations: Stripe Cloudflare Gorgeously simple admin dashboard that is clear and makes sense A small art gallery to represent myself as an artist (only a few pictures for now) Product uploads from varying places (like excel 2003, smh) I've ran it through basic SEO tests to make sure I'm not totally failing. It's live. It will accept payments! -- proud developer moment -- I'm going to share some picks but here is the link: Everfluorescent.com Eeeeeeeeeeee!!!!! Main Page: Custom Admin Dashboard: Let me know if you find a bug! <3

People keep asking how I did it. The honest answer: I didn't "do" anything special. I just shipped something weird and somebody on Hacker News happened to see it. The beginning One year ago I was 14 and bored. I had built maybe 5 "projects" that died on my hard drive. So I spent a Saturday scraping startup names from Product Hunt, pasting them into a JSON file, and throwing together a single HTML page with terrible CSS. I posted it on HN with the title: "Free crunchabse alternative" It hit the front page. 400 upvotes. 600 comments, mostly roasts. A few people actually looked at the directory. Someone asked "how do I add my startup?" I said "I don't know, I just made this in a weekend." That was the first 20 startups listed. The pivot Those 20 startups turned into 200. Then 2,000. I kept answering every comment, fixing every bug reported within 24 hours, and shipping the next feature people actually wanted. Nobody cared that my code was messy. They cared that someone their age was building something they could actually use. Today StartupWiki is now an AI-powered research directory with verified startup profiles, funding data, competitive analysis, and team insights. We just launched our new Launch Platform where startups can submit, verify their badge, and get discovered by the community. And the view count is climbing in a way that still surprises me. The metric everyone obsesses over is the one that's already happened. The real signal? Strangers emailing me asking how to get listed. What I've learned at 15 Ship ugly first. My first version had hardcoded data and a broken CSS gradient. It worked. People didn't care that it was ugly — they cared that it existed. HN is a launchpad, not a home. That first post gave us the initial users. What kept them was the follow-up: answering every request, fixing every bug, shipping based on actual feedback. You don't need permission. I'm 15. I can't rent a car, vote, or legally sign most ToS. None of that stopped me from building