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The real question isn't which framework is better. It's which one you can stop thinking about six months into the project. Key Takeaways Project Suitability — Django is built for weight. Flask is built for speed. Know which one your project actually needs before you commit. Development Flexibility — Django makes decisions so your team doesn't have to. Flask hands those decisions back. Both are features, depending on who's writing the code. Scalability & Performance — Scaling is an architecture problem first, a framework problem second. Pick the one that matches the system you're building — not the one you hope to build. Security Features — Django's protections are on by default. Flask's require you to turn them on. In a fast-moving team, that difference is more significant than it sounds. Ecosystem & Community — Both communities are active and well-documented. You won't be stuck either way. The Decision Nobody Takes Seriously Enough I've watched this play out more times than I'd like to count. A team kicks off a Python project, someone picks a framework — usually the one the most senior person knows best — and everyone moves on. Fast forward six months and the codebase is exhausting to work in. Either they're dragging a full framework through a service that should've been twenty lines of Flask, or they're rebuilding authentication from scratch on something that outgrew its lightweight origins two sprints in. The framework choice isn't irreversible. But undoing it mid-project is expensive in a way that doesn't show up in any estimate. Django and Flask are both genuinely good. What they're good for is different. That's the part worth slowing down on. What You're Actually Getting With Each One Django arrives with almost everything a web application needs already assembled — an ORM, an admin panel, authentication, form handling, CSRF protection, and more. The design assumption is that most web applications need most of these things, so it makes more sense to ship them i

Waymo has a lot of experience building virtual systems to help its autonomous vehicles better understand the real world. It built realistic 3D worlds to better anticipate natural disasters and unpredictable edge cases. It created a virtual representation of a hyperattentive driver to test against its own autonomous vehicles in a series of simulated scenarios […]

Hi, Niels here from the open-source team at Hugging Face. I've recently relaunched paperswithcode.co as a source for finding the state of the art (SOTA) across various AI domains, from 3D generation to AI agents. This is done by automatically parsing research papers published on arXiv/Hugging Face, enabling leaderboards to be created. See BrowseComp below as an example (a scatter plot and a table are available for each benchmark). - Scatter plot (you can hover over the dots to see the models): https://preview.redd.it/9rz2r3ffcf6h1.png?width=2880&format=png&auto=webp&s=b3f8e7a870802f6ef8227ecc0619e9e1057554b0 - Table: https://preview.redd.it/qoqriddw5f6h1.png?width=2862&format=png&auto=webp&s=a0034574f693847537037013672fb61daf27b16e As you can see, I've added support for viewing evals for closed-source models, too, given that many benchmarks are nowadays dominated by them, like GPT-5.5 and Mythos 5. You can always disable viewing closed-source evals with a toggle or in your PwC settings: https://preview.redd.it/p3k6jt6q6f6h1.png?width=1582&format=png&auto=webp&s=40149e51d6b326a77e53e33baf70d9850b3de365 When you turn them off, here's what the open model leaderboard looks like: https://preview.redd.it/tg42sin36f6h1.png?width=2838&format=png&auto=webp&s=1330a117ae9b4e0ce6d459493ae9e8f64107310a Closed-source papers are treated as regular "papers", although they can be any source, like a blog post (given that PwC supports submitting any source beyond arXiv). See the GPT-5.5 or Mythos 5 papers as examples, with their evals at the bottom. Notice the "closed" tag on their evals. Hence, you could jokingly call these "papers without code". Let me know what you think of this, and whether anything needs to be changed or added! Kind regards, Niels submitted by /u/NielsRogge [link] [留言]

Earlier parts examined the performance characteristics of sequential and random access under single-threaded execution, and noted in passing the destructive effect of random access on the TLB. This part devotes full attention to the TLB: what it is, why a TLB miss is more severe than a cache miss, why a page table walk constitutes one of the longest dependency chains a CPU can encounter, how huge pages fundamentally alter TLB reach, and where memory-level parallelism falters in the face of TLB misses. Page Boundaries: Where the Prefetcher Halts Part III, in its discussion of prefetchers, noted a hard constraint: a prefetcher must not cross page boundaries on its own authority. The operating system manages virtual memory in units of pages (typically 4 KB, i.e., 64 cache lines). When a program reaches the end of one page and is about to step into the next, the prefetcher cannot proceed. The reason is that the next page may not reside in physical memory (it may have been swapped out to disk), or it may be an entirely invalid virtual address — if the prefetcher were to speculatively initiate an access to the next page, it would trigger a page fault: the OS would have to suspend the process and swap the page in from disk; in the case of an invalid address, the OS would terminate the process outright. From a security standpoint, the prefetcher neither can nor is permitted to autonomously cross page boundaries without TLB approval. Hence a performance brake appears every 4 KB — even when traversing an array sequentially, after every 64 cache line accesses the prefetch pipeline must pause and await confirmation of an address translation. This is not to say that modern CPU prefetchers are completely unable to cross pages. Intel's Next Page Prefetcher and AMD's equivalent mechanism can consult the TLB when approaching a page boundary — if the address mapping for the next page is already registered in the TLB, the prefetcher receives clearance to continue prefetching across th

Building a "paste a YouTube link, get a transcript" feature sounds trivial until you deploy it to a server. The moment your request comes from a datacenter IP instead of a residential one, YouTube responds with LOGIN_REQUIRED or quietly serves nothing. Here's how VidTranscriber handles it. The problem There are two ways to get text from a YouTube video: Existing captions — if the uploader (or YouTube's auto-caption) provides them, you can fetch the caption track directly. Fast, free, no transcription needed. Transcribe the audio — pull the audio stream and run it through a speech-to-text model (Whisper-family). Works for any video, but costs compute. Both start with talking to YouTube from your server — and that's where it breaks. YouTube aggressively gates datacenter traffic: the watch page and InnerTube API return LOGIN_REQUIRED , and naive audio fetching gets reCAPTCHA'd. The approach The fix is to separate where the request originates from where the work happens : A Cloudflare Worker handles the user request and orchestration. Caption/audio fetching is routed through a path whose egress isn't treated as a bot — so the LOGIN_REQUIRED wall doesn't trigger. Captions, when available, become the primary path (no transcription cost). Only when there are no usable captions do we fall back to downloading audio and running Whisper. Long jobs go onto a queue (Cloudflare Queues) so the request returns immediately and the transcript streams in as it completes. Why captions-first matters Most "transcript generator" traffic is for videos that already have captions — talks, tutorials, news. Serving those from the caption track is instant and free, which means the expensive Whisper path is reserved for the minority of videos that actually need it. That's the difference between a tool that's cheap to run and one that isn't. What's still hard IP reputation drifts — what works today can get throttled tomorrow, so the extraction path needs monitoring and fallbacks. Caption quality

I have watched founders lose sales calls they should have won. Not because they lacked skill. Not because the offer was wrong. Because they walked in to prove they were smart — instead of finding out whether the pain was real. Sales Is Diagnosis Plus Decision The call is not there for you to pitch. The call is there to find out: Is the pain real? Does the buyer have urgency? Does the budget exist? Can a fixed-scope sprint create a clear win? That is it. Four questions. Everything else follows from those. Sales is not pressure. Sales is diagnosis plus decision. 1️⃣ The Call Structure That Works Frame the call in the first 60 seconds: "I'll understand the current state, ask what is costing you, then tell you whether a sprint makes sense. If it doesn't, I'll say so." That sentence does 3 things: Sets expectations — no pressure, no hard close Signals competence — you have done this before Removes the buyer's guard — they can be honest about what is broken Then run this flow: 1️⃣ Current state — what exists now? 2️⃣ Pain — what is broken or slow? 3️⃣ Cost — what does it cost in time, money, trust, or delay? 4️⃣ Urgency — why now? 5️⃣ Decision — who approves? 6️⃣ Success — what would make this worth paying for? 7️⃣ Close — recommend the sprint or walk away 2️⃣ The Questions That Reveal Money These are the 6 questions I use to find whether a sprint is worth recommending: "What happens if this stays broken for another 30 days?" — reveals urgency and cost "What have you already tried?" — reveals how serious they are "Where does the current process lose leads, users, time, or trust?" — reveals the money leak "Who feels this pain most inside the business?" — reveals whether the buyer is also the decision-maker "What would make this an obvious win?" — reveals success criteria before you price "If we fixed only one thing first, what would matter most?" — reveals scope Listen for the answer with the money in it. That is the thing you fix. That is what you price. That is the sprin

A million dollars is emotional as a dream. As math, it is boring. And that is exactly why most people never get close. Break It Down Here is the thing: $1M/year is not one big bet. It is a machine. And machines are built from boring, repeatable components. 20 clients at $50,000? That is $1M. 100 clients at $10,000? That is $1M. 12 retainers at $4,000/month? That is $576k — plus 4 sprints at $10,000 each gets you to $616k. The question is not whether the number is possible. The question is which machine can realistically produce it — from where you actually stand today. 1️⃣ The Practical Ladder Here is how the staged path actually works for an AI service business: Stage What You Are Doing Why It Matters Stage 1 Sell fixed-scope sprints Creates cash and proof Stage 2 Turn repeated sprint work into templates, SOPs, automations Reduces delivery time, increases margin Stage 3 Sell retainers around highest-demand system Predictable monthly cash Stage 4 Productize repeated workflow into software or toolkit Scalable without more hours Stage 5 Scale the thing the market already proved it wants Compound the machine Notice what is missing from Stage 1. There is no SaaS. No product. No cold paid traffic. No team. Just skill, packaged cleanly, sold to people with money and a painful problem. That is the fastest path — not the most glamorous one. 2️⃣ The Proof-of-Force Line The first mission is not $1M. The first mission is $10k/month — reliably, from sprint work. Here is what that actually looks like: 2 × $1,500 teardown/audit packages = $3,000 2 × $3,500 implementation sprints = $7,000 2 × $5,000 launch/GTM sprints = $10,000 3 × $2,000 retainers = $6,000/month That is not the finish line. It is the proof-of-force line. It proves the machine works. It funds the next iteration. It creates the case studies that make the next sprint easier to sell. Then you go from $10k/month to $25k. Then $50k. Then you make the productization decision from a position of demand — not hope. 3️⃣ The

The demo worked perfectly. ✅ Production? First real users. 50% failure rate. ❌ The Gap Nobody Warns You About I see this pattern every week — a founder launches, pushes traffic, and watches their app fall apart in real conditions. Not because the core idea was wrong. Because "it works on my machine" is not a launch-readiness standard. AI-built apps in 2026 ship fast. That is the superpower. But fast shipping without hardening means you are presenting a demo as a product — and real users will find every crack within 48 hours. 1️⃣ What "Launch-Ready" Actually Means Launch-ready is not "the feature works." Launch-ready is when auth, payments, logging, analytics, database permissions, and rollback are boring — because they have already been thought through and tested. Here is the difference: Demo State Launch-Ready State Auth works for happy path Auth handles edge cases, token expiry, role conflicts Payments go through in test mode Webhooks confirmed, retries handled, failures logged Console.log for debugging Structured logging with alerts on errors No analytics Core events tracked from day 1 Manual deploy Automated deploy + rollback path exists No onboarding flow User activation measured from first session If your app is in column one — you are not ready. 2️⃣ The Launch-Readiness Checklist Copy this. Run it before you push traffic. Authentication and authorization — roles, permissions, token handling, session expiry Environment variables — nothing sensitive exposed, prod secrets separate from dev Database permissions — row-level security, no open-read tables, no admin keys in frontend Payment webhooks — test confirmed, failure logged, retry logic exists Error logging — uncaught exceptions surfaced somewhere you will actually see them Analytics events — signup, activation, key action, churn signal — all firing Rate limits — LLM calls protected, API routes guarded Backups and rollback — you have a path back if something breaks Onboarding flow — first session gets the use

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Something worth paying attention to in the Fable 5 launch that I think will get buried under benchmark comparisons. The most consequential line in the AWS announcement wasn’t about context windows or coding performance, it was tucked into the infrastructure section: “Once you opt into data retention, your data will leave AWS’s data and security boundary.” That’s not a model feature, that’s an enterprise architecture constraint. For a lot of companies that sentence alone disqualifies Fable 5 from touching certain workloads no matter how good the model is. The Fable vs Mythos split is also worth sitting with. Same underlying capability apparently, but Mythos is gated behind Project Glasswing and vetted partners only. Anthropic is essentially saying some capability is too sensitive for flat API access, which is a pretty different philosophy than “here’s our best model, go build.” Does the Fable/Mythos split read as responsible deployment to people here or more like managed scarcity? And anyone in enterprise AI already hitting the retention requirement as an actual blocker? submitted by /u/Old_Cap4710 [link] [留言]

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

It kinda creeps me out. Firstly it started from like on chinese word in my chatgpt, now it's fully in chinese? submitted by /u/Oldrus [link] [留言]

I do AI research and keep juggling tabs: new ones on arXiv, trending ones on Hugging Face, famous ones somewhere else again. https://preview.redd.it/cg32bshjqd6h1.png?width=1919&format=png&auto=webp&s=00055bb8af699061be0bdcff59f2cb8fa9ab38b6 So I built one site that brings them all together. Pick a paper, read it right there, star the ones you want for later, and it remembers where you stopped reading, even if you switch from laptop to phone. Live: https://ppdeck.com Demo: https://youtu.be/vtyx34JvxX0 It's free and open source - a star on GitHub would mean a lot ⭐ https://github.com/khuynh22/paper-deck submitted by /u/NeitherRun3631 [link] [留言]