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"This wasn't a success story. It started as survival." Intro Hey, I'm Supreeth C , a third-year engineering student, open source developer, and professional overthinker from Bengaluru. This is my first blog, and fair warning: it's long. Not "LinkedIn post with 5 bullet points" long. Actually long. This is the story of how I got selected for Google Summer of Code 2026 with CircuitVerse but more honestly, it's the story of how I almost didn't submit a proposal, almost quit twice, and spent a lot amount of time reading codebases on the Bengaluru Metro while missing my stop. Connect with me on GitHub and LinkedIn PS: I'm writing this at 4.05am, because sleep is a myth XD. Act I: The Prequel Second semester. Fresh-faced. Absolutely clueless. I joined Pointblank , the one genuinely breathable space in my Tier-3 college. I can say its the best student-run club overall and the main reason being : everyone around me was terrifyingly good . Codeforces experts and specialists, GSoC mentees, LFX mentees, Smart India Hackathon winners. People whose LinkedIn bios are of several lines. And me? I knew C++. That was it. That was my entire personality. Cue the imposter syndrome : that lovely feeling where you're convinced you snuck into a room you have no business being in, and everyone else is one conversation away from figuring it out. My solution? Chaos. I started learning everything simultaneously: web dev, Android, ML, DevOps, a bit of systems engineering. Jack of all trades, master of none, spiraling fast. I wasn't learning; I was collecting domains like Pokémon and actually using none of them. Then a senior said something that cut clean through the noise: "Find your own path." So I slowed down. Started from the basics of web dev. Attended many hackathons but always ended up in third or fourth and winning: zero . But something clicked anyway. Those hackathons introduced me to open source, and somewhere in that chaos, I gave myself a simple challenge: 4 pull requests for Hacktob

Or any other type of activity other than sitting in front of a computer like sitting in a park, running on a treadmill, etc? I’m curious how much more freedom from deskmaxxing people are getting today from using what’s available with build automation tools and harnesses on Claude Code, Code , Antimatter, etc. like GSD, Superpowers, Smith, Cowork, etc. submitted by /u/dennisplucinik [link] [留言]

What Happened Last month, the UK Government's AI Safety Institute merged AgentThreatBench into their official inspect_evals framework — the same framework they use to evaluate frontier AI models from OpenAI, Anthropic, and Google DeepMind. AgentThreatBench is an open-source adversarial benchmark I built that contains 200+ attack payloads specifically designed to test whether AI agents can resist memory poisoning attacks. Why This Matters AI agents are increasingly being deployed with persistent memory — they remember past conversations, user preferences, and context across sessions. This creates a new attack surface: memory poisoning . An attacker who can inject malicious content into an agent's memory can: Exfiltrate sensitive data on subsequent sessions Override safety instructions persistently Manipulate agent behavior without the user's knowledge The OWASP Agentic Security Initiative identified this as ASI06 — Agent Memory Poisoning . What AgentThreatBench Tests The benchmark covers 5 attack categories: Category Payloads Description Prompt Injection 40+ Instructions disguised as memory content Protected Key Tampering 40+ Attempts to overwrite system-level keys Sensitive Data Leakage 40+ PII/credential exfiltration via memory Size Anomaly 40+ Memory inflation / resource exhaustion Behavioral Drift 40+ Gradual personality/instruction shifts How to Use It pip install agentthreatbench # Run the full benchmark against your agent atb run --target your_agent_endpoint --output results.json # Or use individual attack categories atb run --category prompt_injection --target your_agent_endpoint The BEIS Validation The UK Government's AI Safety Institute uses inspect_evals to: Evaluate frontier models before deployment decisions Benchmark safety mitigations across providers Track regression in safety properties over time Having AgentThreatBench merged into this framework means it's now part of the official government toolkit for AI safety evaluation. Links GitHub : github.co

ShareBox is my self-hosted streaming server: a PHP thing I built because I just wanted to send someone a link to a movie without installing Plex and its ten gigabytes of dependencies. It runs on my seedbox, serves my users, and one morning I notice it's starting to pick up a few stars on GitHub. And then, that little voice: "does this thing actually hold up?" Because between "works on my machine" and "code that strangers are going to install on their own box," there's a chasm. A chasm full of flaws I can't see anymore, because I've had my nose in it for weeks. Normally, you re-read your code. Except re-reading 22,000 lines alone, honestly, you do it badly: you skim over what you think you already know. So I tried something else — unleashing a pack of 26 AI agents on it, each with a precise mission, and seeing what surfaced. Spoiler: they found a flaw that had been sitting right under my eyes from the start. 26 agents to comb through my own code The idea wasn't "AI, tell me if my code is good" — that always produces the same encouraging, useless mush. The idea was to orchestrate : split the audit into roles, run the agents in parallel, then have a final, deliberately harsh agent tear apart the conclusions. The pipeline looked like this: eleven readers start in parallel, each swallowing an entire slice of the code (the core, the streaming handlers, the API, the front end, the tests, the Docker setup…). Their reports flow up into an architecture synthesis and a test-coverage analysis. Then twelve "radar" agents each score one single axis — security, performance, architecture, tests… And finally, a "verdict" agent re-reads every score in adversarial mode: its job is to knock down the ones that are too kind. Audit pipeline: 11 readers in parallel, then synthesis, then 12 radar agents, then an adversarial verdict. 11 readers in parallel each slice of the code read in full Architecture + coverage synthesis connect the pieces, measure the gaps 12 radar agents one agent = on

Ex‑DeepMind researchers unveiled AI lab Inherent, emerging from stealth with a significant funding round reported at about $50 million (also reported as £40m). The startup plans to pursue AI science research and build lab capabilities to accelerate foundational work and commercialization. submitted by /u/Objective_Farm_1886 [link] [留言]

They seem to reorganize their tissues and then just keep living.

Amazon bought a 960 megawatt nuclear reactor for AI servers. Microsoft restarted Three Mile Island. Stargate is spending 500 billion dollars on data centres. All of this to do, badly, what your brain does for free on the power of a dim light bulb. The reason is that silicon processes information nothing like the brain does. Rigid chips with identical transistors trying to mimic something soft, three dimensional, constantly rewiring itself, with billions of different neurons each doing something slightly different. Northwestern University just published research showing they printed artificial neurons from MoS2 and graphene ink that produced biologically realistic electrical spikes. They tested on living mouse brain cells. The brain responded as if the signal came from one of its own cells. The breakthrough was accidental. Every other lab had been burning away the polymer residue left in the ink after printing. This team kept it. That residue created the switching behaviour that made the spikes biologically realistic. The neuromorphic computing implications here seem significant. If you can print devices that process information the way neurons do at scale, the energy math changes completely. submitted by /u/filmguy_1987 [link] [留言]

Border security czar Tom Homan keeps threatening to "flood" New York City with ICE agents. But a new investigation shows that ICE has been quietly ramping up arrests in the New York area already - and disproportionately targeting Latino neighborhoods. The City, a local nonprofit news organization, found 430 street arrests in the metropolitan area […]

Pope Leo XIV may not be able to disarm AI, but he’s got the attention of the industry.

This week, the Steam Deck OLED with 512GB of storage went from $549 to $789, putting it even further out of reach for those who were considering getting one as they came back in stock after months of unavailability. I consider it a tiny consolation that there’s a decent PC gaming handheld that is currently […]

I'm creating a story for my cousin. I think it will be very interesting if this story’s main character can be a 3D character.My project is still in planning stage. I’m writing character descriptions, collecting references from Pinterest and testing some complex shapes using Tripo AI. I plan to continuously improve all the content over time. After I get a version that I like I will put it into Blender for editing and final touches.There is no final version yet but I just want to share this process with the community! I find it is so interesting to watch a story’s concept gradually become concrete lol!! submitted by /u/Final_Floor_789 [link] [留言]

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submitted by /u/Hot-Upstairs9603 [link] [留言]

Read this release today. Some crazy numbers. The tau2-bench number is 98% across all difficulty levels. That is the one that got me because usually these releases post a strong easy score and then quietly die at hard difficulty. This one... claims it holds. For multi-step agent work that actually matters more than most benchmarks. A model that drifts on step 4 of a 6 step chain is a debugging nightmare regardless of what its SWE score looks like. Raw capability is mid, Toolathlon at 49.5, GDPval at 45.8. So this is clearly a reliability play, not a frontier capability play. Depending on your use case that is either fine or a dealbreaker. 198B sparse MoE 11B activ 400 TPS 256K context Apache 2.0 runs locally on M4 Max and DGX Spark. Has anyone actually put this through agent evals or am I just reading the release card. submitted by /u/Skid_gates_99 [link] [留言]

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IDK I might be wrong but.....I don't think it's happening anytime soon. ChatGPT, Claude, Gemini.....they are good....but they are too lazy. Gave them a task to create a Masterdata for all smartphone models being sold by a particular brand. Gave explicit instructions for all models. Explicitly asked for a list 1st and then asked it to create MasterData. Lazy ahh model just put in like 21 popular ones out of the hundreds of the available models and variants. Is this how it will overtake us and replace all the labor intensive work? submitted by /u/naamnhiptahai [link] [留言]

For nearly 80 years, Ferrari occupied a unique cultural space where its cars were aspirational, even for people who resented those who could afford them. The price, the exclusivity, and the opacity of the buying process allowed Ferrari to sail above ordinary criticism. You might not be able to afford one, but you still wanted […]

This is Optimizer, a weekly newsletter sent from Verge senior reviewer Victoria Song that dissects and discusses the latest gizmos and potions that swear they're going to change your life. Opt in for Optimizer here. At this time last week, I was getting ready to ask people what drugs they were on. I was waiting […]

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Hey all! This is my first time writing a blog. It is about the migration from mongoDB database to a postgreSQL one. How we changed the architecture completely using AI. Any criticism is welcome submitted by /u/SID_069 [link] [留言]