标签：#programming

submitted by /u/broken_broken_ [link] [留言]

I recently read some interesting research on GitHub's Verified commit workflow. The issue isn't a break in Git, GPG, or commit signing itself, but rather how the Verified badge can be interpreted in certain edge cases. It's a good reminder that a cryptographically valid signature doesn't automatically establish the provenance or intent of a commit. Here's a technical breakdown covering how the trust model works, the affected scenarios, GitHub's response, and what maintainers can do to avoid relying solely on the Verified badge during code review. submitted by /u/NapierPalm [link] [留言]

submitted by /u/Ordinary_Leader_2971 [link] [留言]

Most web applications contain at least one vulnerability from the OWASP Top 10. A typical security audit takes 2-3 weeks and costs upward of $10,000. An LLM can compress the initial audit down to a few hours because it scans code for patterns rather than specific CVEs. Below are 15 vulnerabilities found while auditing production code with Claude. Each includes the vulnerable code, the fixed version, and a prompt to reproduce the finding. Classification follows OWASP Top 10 (2021). Order reflects frequency of occurrence: most common first. Methodology: how to run an AI security audit The audit consists of three passes. First, a broad scan: the LLM receives the entire project and looks for vulnerability patterns. Second, deep analysis: each identified pattern is verified in context (middleware, ORM, framework). Third, verification: manual review of every finding, because LLMs produce false positives. Prompt for the broad scan: Perform a security audit of this code. For each finding, include: 1. CWE ID and name 2. OWASP Top 10 category 3. Severity (Critical/High/Medium/Low) 4. The vulnerable code snippet 5. Attack vector -- exactly how an attacker would exploit this 6. Fixed code Ignore stylistic comments. Focus on security only. Start with injection attacks, then broken access control, then the rest. This prompt works because it defines the output structure and prioritizes categories. Without explicit instructions, the LLM mixes critical vulnerabilities with remarks about email validation. More on structured AI code review: AI Code Review Checklist . A03:2021 -- Injection 1. SQL Injection via string concatenation The most common finding. Shows up even in projects using an ORM, because developers switch to raw queries for complex filters. Vulnerable code: // API endpoint for user search app . get ( ' /api/users ' , async ( req , res ) => { const { search , sortBy } = req . query ; const query = ` SELECT id, name, email FROM users WHERE name LIKE '% ${ search } %' ORDER

I'm writing a short series of tutorials on FlashAttention, the algorithm that largely powers modern LLMs. The core idea is to spot the associative structure hiding in the loop. Once you see it, you get two things: you can fuse the passes (in the case of attention, it is huge, because it avoids ever materializing the score matrix) and split and recombine the work across GPU threads or any parallel processor. This post is about recognizing that structure. The stable softmax operation on a vector x (part of the attention kernel) is computed as follows: m = max_j x_j softmax(x)_i = exp(x_i - m) / Σ_j exp(x_j - m) Naively, you would write something like: # pass 1 — running max m = -inf for j in 0..N: m = max(m, x[j]) # pass 2 — denominator, needs the final m d = 0 for j in 0..N: d += exp(x[j] - m) # pass 3 — normalize for j in 0..N: y[j] = exp(x[j] - m) / d The trick is not to wait for the final max before accumulating the denominator. Carry both in a small state (m, d) and rescale d whenever the max moves. Here is what the online version of the algorithm above looks like: # one pass — carry (m, d) together m, d = -inf, 0 for j in 0..N: m_new = max(m, x[j]) d = d * exp(m - m_new) + exp(x[j] - m_new) # rescale, then add m = m_new # normalize (unchanged) for j in 0..N: y[j] = exp(x[j] - m) / d It turns out this is not a one-off trick. There is a whole class of "secretly associative" loops that you can parallelize by introducing the right carrier state. The tutorial goes into detail, shows a few examples of secretly associative operations, provides the algebraic formulation for those, and gives some tools to help you recognize secretly associative loops. Overview: Safe softmax, Welford's variance, and FlashAttention belong to the same class of secretly-associative operations The twisted monoid via transport of structure, why the max-rescale coupling doesn't break associativity Third Homomorphism Theorem as a test for whether any loop is secretly associative Numerical analys

I'll preface to say I'm not at all a programmer, so pardon me for saying anything stupid The increasing amount of slop is no secret, and but software as a whole, be it plugins or apps or even whole operating systems, has become increasing plagued by a set of kinda-unrelated problems that feel like they fall under the same umbrella. I'll keep it short, so practical issues of efficiency, privacy, security, and long-term support, and then ethical issues of being human-made. I personally believe that we're in need of novel, widespread terms to be able to describe the kind of software that in principle does not have certain traits. I attached the link because I believe the term "open source" is analagous to what I'm talking about here. Everybody has an idea of what it means and entails, but it also doesn't mean that everything has to strictly follow it to a T, because it's just a word at the end of the day. It's a pledge to the user to abide by principles of being "Open Source," and I'm saying that we need new words that similarly serve as principles such as that of, optimization, long-term support, being human-made, etc. (It's kind of like how we have terms like organic, free-run eggs, free range eggs, and of course you would end up with having a bunch of terms that dont mean anything at all, but i'm confident that a few will emerge as "standards" in the same way the term open source is.) This could go hand-in-hand with maybe making it common practice to at least disclose the development process when publishing software. In (science) research, we're beginning to see the implementation of "AI Disclosures" to detail all such usage, to be published with the paper. surely practices like these should be encouraged in software development as well. I'm sure there are (smaller) discussions on what exactly should constitute as "best practices" to the people that care about such things, but I've yet to see mainstream terms that go beyond only vaguely describing what we dont like

How is it going for you all working on projects for this platform? I’ve read that sometimes they ban you without even giving you a heads up, is that true? Also, I’d like to ask about the tests they give you for software projects before accepting you; are they actually very difficult? Do I need to have AI level problem solving skills or something similar to get accepted? How is the hourly pay working out? Is it truly hourly, and are deposits made weekly? Thanks for reading, everyone; I’m curious about remote work and want to get started. submitted by /u/ClarkKennedy929 [link] [留言]

submitted by /u/lood9phee2Ri [link] [留言]

Two weeks ago one of my outdoor cats bit me. She's fine — healthy, pregnant, and deeply offended that I picked her up, but she needed flea medicine and I needed to confirm the pregnancy. (If anyone wants a kitten, I know a grumpy lady who has some.) My pinky swelled up, and typing went from "mildly error-prone" to "not happening." So I dictated this post. If you've ever looked at raw voice transcription, you know what that produces: one giant unpunctuated block with half the words wrong. My transcript literally claims "AI needed to put flea medicine on her." It was me. That's the kind of thing the AI is cleaning up. The ideas are mine. The argument is mine. The punctuation and clarification belongs to the machine, because the machine is better at punctuation than a transcript is. By the rules of the current discourse, you're now supposed to stop reading. That's the game, right? "Not reading this if it's AI-generated." "It has em dashes — slop." Let's deal with the em dash first, since it's apparently forensic evidence now. You can type one. Shift-Option-hyphen on a Mac. Windows-Shift-hypen on Windows. Writers were littering pages with them for a century before the first transformer shipped. Its little brother the en dash is everywhere too, and nobody has ever accused an en dash of being a robot. The em dash gets singled out for exactly one reason: it's a fast, cheap way to judge a piece of writing without engaging with a single idea in it. Zero effort, instant superiority. Remember that phrase — zero effort. It's coming back. Because real AI slop absolutely exists. Someone fires off one prompt, ships whatever falls out, never reads it, then farms for stars and upvotes. That's slop — not because a model was involved, but because no human was. Effort is the variable. The tool never was. Here's what the other end of the spectrum looks like. Hundreds of hours on a single project. I decide the architecture, the language, how it compiles, how it deploys. I fork the output

I rebuilt aesencryption.net so text AES (128/192/256) runs fully in the browser - the key and plaintext never leave the page. The hard part is staying byte-compatible with common server-side AES libraries (mode, IV, padding, base64 output), so I ship copy-paste equivalents in PHP, Java, Python, Go, Rust, Kotlin and JS. Live tool (mine, free): https://aesencryption.net - feedback on the crypto choices welcome. My own site.

I've been working on rootless container checkpoint/restore in CRIU and wrote up some notes on the prototype, major failures, and the path toward the current implementation. Would appreciate any feedback, criticism, and corrections, especially from people familiar with CRIU, Podman, or container runtimes. submitted by /u/Ok-Job3177 [link] [留言]

So a few days ago I was in a comment thread arguing about whether AI is quietly ruining junior...

Deceive the enemy with an obvious approach that will take a very long time, while ambushing them...

Where should your auth token live so an XSS bug can't steal it? Here's how to build auth that survives the crazy non-secure world we live in. submitted by /u/creasta29 [link] [留言]

We're back with another installment of the DEV Weekend Challenge ! If you missed the earlier editions, these are short-form, high-energy challenges designed to fit right into your weekend. We're giving you the heads-up now so you can clear your schedule! How It Works Our challenge prompt will be revealed at launch. Follow #weekendchallenge for updates. You can also keep an eye on the DEV Weekend Challenge page or look out for the official announcement post from the DEV Team . From there, you'll have the entire weekend to build, document, and submit your project. That's all there is to it! Because our community spans every timezone on the planet, we've set the window so that everyone around the world gets at least a full weekend to participate. Important Dates Launch Time: July 10 at 2:00 AM UTC Submissions Due: July 13 at 6:59 AM UTC Here's what that looks like across a few timezones: Timezone Launch Time (Local) Submissions Due (Local) PDT Thursday, Jul 9 at 7:00 PM Sunday, Jul 12 at 11:59 PM EDT Thursday, Jul 9 at 10:00 PM Monday, Jul 13 at 2:59 AM GMT Friday, Jul 10 at 2:00 AM Monday, Jul 13 at 6:59 AM CEST Friday, Jul 10 at 4:00 AM Monday, Jul 13 at 8:59 AM IST Friday, Jul 10 at 7:30 AM Monday, Jul 13 at 12:29 PM JST Friday, Jul 10 at 11:00 AM Monday, Jul 13 at 3:59 PM AEST Friday, Jul 10 at 12:00 PM Monday, Jul 13 at 4:59 PM While the window technically spans more than 48 hours, our goal is to ensure everyone has a full, uninterrupted weekend to work on their project regardless of where they live. What else is happening? Mark your calendars for the upcoming Summer Bug Smash . Bug Smash - Register Now We can't wait to see what you build!

submitted by /u/c1rno123 [link] [留言]

Everyone does CV in Python/C++. I did it in Rust anyway – and it worked. 🦀 Libraries compared, code breakdown, and why I chose kornia‑rs submitted by /u/darryldias [link] [留言]

Calw-Code is one of the fastest-growing repos in the world When Claude's source code was leaked, some developers studied it and recreated this AI. Claw--Code written mostly in Rust submitted by /u/Ok_Stomach6651 [link] [留言]

submitted by /u/itgforlife [link] [留言]

Nephew saw a YouTube ad. Someone was selling a "secret prompt" for ₹199, claiming ChatGPT and Claude can analyze the stock market and place trades with 90% accuracy — no technical analysis, no fundamentals, just paste this prompt. He brings it straight to Uncle. The Ad 👦 Nephew: Uncle, I saw an ad on YouTube. Some guy was saying, "Use ChatGPT and Claude AI for stock market analysis, take trades with 90% accuracy. You don't even need to know technical analysis or fundamentals — just use this prompt and you'll get all the results." Is that actually possible? 👨‍🦳 Uncle: (laughs) Ah, here we go. This is exactly how a lot of scams happen — and honestly, it's rarely because of some clever new invention. It's because of a lack of understanding, and people treating these models as a magic black box. 👦 Nephew: So are they scamming us? Or genuinely fooling themselves too? 👨‍🦳 Uncle: Not exactly a straightforward scam, and not exactly genuine either. Here's the honest split: they're maybe 30% correct, and 70% wrong. 👦 Nephew: What does that even mean? 👨‍🦳 Uncle: I'll accept this much — there genuinely are AI models out there that can do a solid job predicting stock trends or running fundamental analysis, because that kind of prediction is heavily mathematical, numerical work. But — and this is the important part — ChatGPT, Claude, and Gemini are not that kind of model. 👦 Nephew: Why not? It can literally write code. It can do math inside code. Why can't it just... do math for stock prediction too? I genuinely don't get it. 👨‍🦳 Uncle: Come, sit. This needs a proper, from-scratch conversation. We're going to dig all the way down to what these models actually are , and by the end, you'll understand exactly why ChatGPT, Claude, and Gemini are the wrong tool for this specific job — not a scam exactly, but sold by people who never actually opened the box themselves. Part 1: What Is an LLM, Really — In One Honest Sentence 👨‍🦳 Uncle: Before anything else, one sentence, and hold onto i