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Hi everyone, I'm an undergraduate student and ML researcher at UC Berkeley. My colleagues and I are working on a project that hopes to fix some of the problems users face with Colab. What are the features you wish it had as an ML professional, researcher, or enthusiast? What're the biggest problems you've faced while using it? Some of the issues that everyone feels (including us) is environment management and kernel persistence. But we would love to hear more from the community. submitted by /u/myplstn [link] [留言]

Quiet Defaults, DNSSEC Cracks, and Agents in the Data Plane I read the AWS Nitro V6 TCP timeout change twice before I believed it. Default went from 432,000 seconds to 350 seconds. Five days to six minutes. On the newest instance family. Quietly, in release notes most people won't read until something breaks. That sort of set the tone for May. No flagship launch to anchor the month around. What there was a lot of: defaults moving in places vendor press releases don't celebrate. Post-quantum crypto pushing into campus boot chains. Every cloud vendor shipping some flavor of agentic-networking pattern. The .de TLD briefly breaking because of DNSSEC. None of it announced loudly. All of it the kind of thing that breaks production at 2am if you weren't paying attention. What Moved This Month Three things, fast. Post-quantum crypto left the VPN tunnel. Cisco's full-stack PQC for campus and branch is the next chapter after April's PQ IPsec story — boot, firmware signing, supply chain attestation, and transport-layer crypto all moving together. If your campus has mixed-vintage gear (which is basically everyone), this is multi-year partial coverage with no clean switchover. Agentic networking became a real category. Cloudflare's Town Lake / Skipper writeup and Claude Managed Agents , Palo Alto's Portkey-based unified AI Gateway , and AWS's Bedrock AgentCore connectivity patterns all dropped this month. The right question stopped being "can my agent reach the model" and became "what IAM blast radius does this agent have if it gets prompt-injected." DNSSEC had a rough month. The .de TLD broke briefly, the DNSSEC root key was rolled, and Cloudflare also debugged a QUIC CUBIC death spiral that was hiding in plain sight. The Internet's core had a louder month than usual, and not in a good way. 1. Agentic AI Is Now Actually A Networking Problem An agent in production isn't a fancy chatbot. It's a thing that calls APIs, reads logs, accesses SaaS data, and sometimes writes back to sy

🌐 Read this post in Bahasa Indonesia here . 📝 A note on this article This post is based on my personal study notes on version control and Git collaboration. To make these notes more readable and useful — for myself and for others — I worked with AI to help expand and structure them into a proper blog format. The ideas, learning journey, and understanding are mine; the AI helped with the writing and presentation. Learning Git doesn't have to be intimidating. In this article, I'll break down the essential concepts of version control and collaboration — using simple analogies that anyone can understand. What Is Git? Git is a version control system . Think of it as a save system for your code — like save points in a video game. Every time you save (commit), Git remembers the state of your project at that moment. If something goes wrong, you can always go back. Repository: Your Project's Warehouse A repository (or "repo") is the folder that Git watches. There are two types: Local repository : lives on your computer. Your personal workspace. Remote repository : lives on a server (GitHub, GitLab, Bitbucket). The "official" shared copy your team can access. They stay connected through a Remote URL, so you can push your local changes up and pull others' changes down. git init # Start tracking a folder git remote add origin <url> # Connect to a remote repo git push origin main # Send commits to remote git pull origin main # Get latest from remote Commit: Your Project's Save Point A commit is a snapshot of your project at a specific moment. Each commit has: A message describing what changed A unique ID (hash) A timestamp git add . # Stage all changes git commit -m "Add homepage layout" # Save a snapshot git log --oneline # View commit history Write meaningful commit messages. Future you will thank present you. Checkout, Reset, Revert: Traveling Through Time These three commands all interact with your commit history — but in very different ways: git checkout — Visit the Past (T

🌐 Baca artikel ini dalam Bahasa Inggris di sini . 📝 Catatan tentang artikel ini Artikel ini dibuat berdasarkan catatan belajar pribadi saya tentang version control dan Git kolaborasi. Untuk membuat catatan tersebut lebih mudah dibaca dan bermanfaat — bagi saya dan orang lain — saya menggunakan bantuan AI untuk mengembangkan dan menyusunnya menjadi artikel blog. Ide, perjalanan belajar, dan pemahamannya adalah milik saya; AI membantu di bagian penulisan dan penyajiannya. Belajar Git itu tidak harus membingungkan. Di artikel ini, saya akan menjelaskan konsep-konsep penting dalam version control dan kolaborasi menggunakan bahasa yang sederhana — bahkan dengan analogi yang bisa dipahami anak kecil sekalipun. Apa Itu Git? Git adalah version control system — sistem yang merekam setiap perubahan yang kamu lakukan pada file-file proyekmu. Bayangkan Git seperti fitur save point di video game. Setiap kali kamu menyimpan (commit), Git mengambil "foto" dari kondisi proyekmu saat itu. Kalau ada yang salah, kamu bisa kembali ke foto sebelumnya. Repository: Gudang Proyekmu Repository (atau "repo") adalah folder yang diawasi oleh Git. Ada dua jenisnya: Local repository : ada di komputermu sendiri. Ruang kerja pribadimu. Remote repository : ada di server (GitHub, GitLab, Bitbucket). Salinan "resmi" yang bisa diakses seluruh tim. Keduanya terhubung lewat Remote URL, sehingga kamu bisa mengirim perubahan ke remote ( push ) atau mengambil perubahan terbaru dari sana ( pull ). git init # Mulai memantau sebuah folder git remote add origin <url> # Hubungkan ke remote repo git push origin main # Kirim commit ke remote git pull origin main # Ambil update terbaru dari remote Analogi: Local repo adalah buku sketsamu di rumah. Remote repo adalah papan pengumuman kelas — semua orang bisa melihat dan mengaksesnya. Commit: Save Point Proyekmu Commit adalah snapshot dari kondisi proyekmu pada satu titik waktu. Setiap commit berisi: Pesan yang menjelaskan apa yang berubah ID unik (hash) Timestamp g

Fundraising, GTM, and bounties for startups Discussion | Link

This is a submission for the GitHub Finish-Up-A-Thon Challenge What I Built I built Academia, a modern, delivery-only Learning Management System (LMS) designed for creators who want a premium, distraction-free environment for their students. With Academia, educators can spin up their own custom academy, build out rich curriculums with video and text lessons, and securely invite students via email magic-links. Once a student accepts an invite, they are dropped into an isolated, hyper-clean student portal that tracks their course progress in real-time. This project started two years ago during a hackathon. I had this really great idea, but I was still very much a beginner. Between the tight timeframe, lack of sleep, and my slow coding speed, I just couldn't pull through to finish the project in time to submit. It sat abandoned in my repositories, gathering dust. Finishing it means finally delivering on the exact vision I had in my head two years ago, but with the skills and architecture of today. Demo Live Site Repository The Comeback Story I always told myself I’d finish this project "after the dust settles," but I never got around to it. When I saw this challenge, I knew it was time. Just looking at the 2-year-old repository gave me an instant headache. The packages were entirely outdated, Vercel was throwing massive build errors, and running npm run dev was painfully slow. Basically, everything was lagging. I seriously considered just starting a fresh repo from scratch. I figured I would just look at the shabby design I created two years ago and try to manually copy-paste the "important" code over, because I knew updating the existing mess would be an absolute nightmare. But instead, I decided to lean heavily on GitHub Copilot Auto (so I could automatically get the best models for the job) to see if we could salvage and modernize the original codebase. Here is the story of my before-and-after journey, and how Copilot helped me finally finish what I started. My Expe

After running AI coding agents in production for a while, one thing became clear: the failures aren't in the code the model writes. They're at the seams — git, CI, auth, the network. The boundaries with the outside world. The model itself is genuinely capable. It writes functions, writes tests, refactors. What breaks is everything around the work: pushing the result, waiting on CI, merging the PR, refreshing a token, calling another service. And the failures are often the kind a human would avoid without thinking. Here are five incidents we hit and fixed in Codens' Purple (the orchestration core) over the last few weeks. All real, with production task IDs and dates. Every fix is merged. There's a shared design lesson at the end that ties them together. Incident 1: a half-resolved merge nearly flooded a PR with 12,000 lines This was the scary one. A Purple task on opsguide-back opened a PR. I looked inside: +12,162 lines / 149 files changed, with literal <<<<<<< markers in 2 of them . The commit graph: e567ce67 (merge commit, "chore: Fix HYBRID_SEARCH...") ├ parent[0] = 0b069e5d (develop tip, +1468 commits over main) └ parent[1] = 2940de35 (the actual feature commit) What happened: in the fix step, the AI decided to git merge develop to backport some test fixes. The merge conflicted. The AI resolved it partially and drove git commit through anyway with markers still in the tree. What got pushed: develop's entire divergence plus unresolved conflict markers. If anyone had clicked merge, main would have been polluted by 1468 commits of develop drift in one shot. A human wouldn't do this. They wouldn't merge develop into a main-targeted PR in the first place, and if it conflicted they wouldn't commit until it was fully resolved. But the AI, optimizing locally to get one test passing, does it without hesitation. Fix: stop it at push time, in two layers A single git pre-push hook. This is where the AI's git push actually goes, so this is where the guard belongs. #!/bin/bas

1.4 billion iPhones. That's how many devices Apple will push Siri 2.0 to this fall—built on a custom 1.2-trillion-parameter Gemini model and an Extensions system that lets users route Siri's AI brain to Claude, ChatGPT, or Google's own Gemini directly. Tim Cook announced all of it at WWDC 2026 on June 8, his final WWDC keynote before handing the CEO role to hardware chief John Ternus in September. The story isn't just a better Siri. Apple is opening iOS as an AI distribution channel—the largest in history—and the developer implications are immediate. The Gemini Deal Apple licensed a custom 1.2-trillion-parameter Gemini model from Google at a reported $1 billion per year. That's eight times the parameter count of Apple's current 150-billion-parameter on-device foundation model. Bloomberg first reported the deal in March 2026; WWDC confirmed it on stage. The private vs. cloud distinction matters here. Apple isn't routing your queries to Google's servers. The custom Gemini model runs on Apple's Private Cloud Compute (PCC) infrastructure—Apple-owned silicon, Apple-controlled software, with cryptographic attestation that prevents even Apple engineers from reading query contents. This is the same architecture Apple built for existing cloud AI features, now scaled up for a model eight times larger. What that means in practice: Siri can answer complex multi-step questions, reason over your personal context (calendar, messages, mail), and execute cross-app actions. None of it touches Google's data centers. The New Siri App Siri in iOS 27 ships as a standalone app. iMessage-style interface, persistent conversation threads, full chat history synced via iCloud. Two modes: quick invocation through the Dynamic Island (a glowing "Search or Ask" prompt) for fast queries, and the full chat app for extended conversations. Chat-mode Siri is a first-party ChatGPT competitor. Persistent threads, file attachments, on-screen awareness. Ask "what does this error mean?" while looking at an

Clean Architecture on the Frontend: Beyond Smart and Dumb Components djblackett djblackett djblackett Follow Jun 7 Clean Architecture on the Frontend: Beyond Smart and Dumb Components # react # frontend # architecture # typescript Comments Add Comment 14 min read

The signal hidden in this week's GitHub trending Two agent-shaped repositories cracked the daily GitHub trending board this week. The first is mvanhorn/last30days-skill , a Claude-style skill that researches a topic across Reddit, X, YouTube, Hacker News, and Polymarket, then synthesizes a grounded summary. The second is NousResearch/hermes-agent , billed as "the agent that grows with you" — a persistent agent runtime that compounds context across sessions. Both ranked the same week. Both are skill-shaped: a manifest, a trigger, a set of instructions, and a runtime expectation. This is the first time I have seen two skill repos chart simultaneously on GitHub trending. Most observers will treat them as cool side projects, fork them, star them, and move on. They are cool side projects. They are also a phase transition that the agent ecosystem has been edging toward for nine months. By Q4 you are going to wish you had read this signal in early June, because the dependency-graph problem about to land in production agents is the same one the npm ecosystem ran into between 2011 and 2018 — except faster, less tooled, and with a much larger blast radius. This post is about that phase transition. The benchmark coverage of skills is everywhere; what you cannot easily find is a working operational model for managing them at fleet scale. I am going to give you one. What actually shipped this week Let me anchor on the facts before I extrapolate. last30days-skill (mvanhorn) is a single skill bundle. Its SKILL.md tells the host agent: when the user asks for recent news, controversy, or sentiment on a topic, run a structured multi-source fetch — eight queries minimum, across five platforms, with a freshness window of 30 days — then synthesize. The skill ships with prompt scaffolding, query templates, and a synthesis rubric. It is roughly 600 lines including instructions and helper scripts. Installation is a git clone into your skill directory, no package manager, no version negotia

Here's the maths most calculators get wrong Most freelance rate calculators are wrong. Not buggy — conceptually wrong. They take your income goal, divide by hours, and hand you a number that will quietly lose you money all year. I got tired of explaining the correct calculation to freelancer friends, so I built a tool that does it properly. Here's the logic behind it, and a bit about how I built it. The flawed formula The typical calculator does this: hourly_rate = annual_income_goal / annual_hours_worked So if you want $80,000 and work 2,080 hours a year (40 × 52), it tells you to charge ~$38/hr. This is wrong in three separate ways. Fix 1: Tax is not optional Your income goal is a net number — what you want to keep. But you're taxed on gross. So the first correction is grossing up: const grossIncome = netTarget / ( 1 - taxRate ); // $80,000 / (1 - 0.28) = $111,111 That's already a $31,000 gap the naive formula ignores. Fix 2: You don't bill every hour you work This is the big one. Freelancers bill roughly 55–65% of their working hours. The rest is admin, proposals, invoicing, sales calls, and learning. const totalHours = weeksWorked * hoursPerWeek ; // 46 * 40 = 1840 const billableHours = totalHours * billableRatio ; // 1840 * 0.6 = 1104 So your real billable capacity isn't 2,080 hours. It's closer to 1,104. Pricing on the wrong denominator is how people end up working 50-hour weeks and still missing their income target. Fix 3: Business expenses come out of gross Software, hardware, insurance, courses — all real costs that need covering before you pay yourself: const totalNeeded = grossIncome + annualExpenses ; The corrected formula Putting it together: function minimumRate ({ netTarget , taxRate , expenses , weeks , hoursPerWeek , billableRatio }) { const gross = netTarget / ( 1 - taxRate ); const totalNeeded = gross + expenses ; const billableHours = weeks * hoursPerWeek * billableRatio ; return totalNeeded / billableHours ; } minimumRate ({ netTarget : 80000 ,

If you’ve ever spent hours debugging slow EC2 workloads or getting sticker shock from unexpected EBS IOPS charges, you’ve probably wondered if there’s a better storage option for temporary, high-performance data. AWS EC2 Instance Storage (also called Instance Store) is one of the most underutilized but powerful tools in the EC2 ecosystem—if you know how to use it correctly. This guide breaks down everything you need to know: core concepts, performance optimizations, use cases, limitations, and how it stacks up against EBS. By the end, you’ll be able to cut storage costs, boost workload performance, and avoid costly data loss mistakes. Table of Contents What Exactly Is AWS EC2 Instance Storage? Core Concepts of EC2 Instance Store Key Features That Make Instance Store Stand Out Which EC2 Instance Types Support Instance Store? Deep Dive: NVMe SSD Instance Store Volumes SSD Instance Store Performance Best Practices EC2 Instance Store vs EBS: Head-to-Head Comparison Top Real-World Use Cases for EC2 Instance Store Critical Limitations to Avoid Costly Mistakes Production-Grade Best Practices for Instance Store Root Volume Options: EBS-Backed vs Instance Store-Backed Instances EC2 Instance Store Pricing: No Hidden Costs Conclusion References What Exactly Is AWS EC2 Instance Storage? EC2 Instance Store is temporary block-level storage that is physically attached to the host server running your EC2 instance. Unlike standalone storage services like EBS, EFS, or S3, it is part of the EC2 service itself, with no network overhead between your instance and the storage disks. Its defining trait is its ephemeral nature: data stored on Instance Store only persists for the lifetime of the associated instance. If you stop, hibernate, or terminate your instance, all data on Instance Store volumes is permanently deleted. Core Concepts of EC2 Instance Store Before you start using Instance Store, make sure you understand these foundational rules: Device naming : Instance Store volumes are

PostgreSQL Error 2200D: invalid escape octet The 2200D: invalid escape octet error occurs in PostgreSQL when a bytea value contains an invalid escape sequence. This typically happens with the legacy escape format for binary data, where octet values must be represented as three-digit octal numbers in the range \000 to \377 . If the escape sequence falls outside this range or uses non-octal digits, PostgreSQL raises this error immediately. Top 3 Causes 1. Out-of-range octal values in bytea escape literals The bytea escape format only accepts octal values from \000 to \377 (decimal 0–255). Using values like \400 or non-octal digits like \9 will trigger this error. -- BAD: \400 exceeds valid octal range (max is \377) SELECT E ' \\ 400' :: bytea ; -- ERROR: invalid escape octet -- BAD: \9 is not a valid octal digit SELECT E ' \\ 9AB' :: bytea ; -- ERROR: invalid escape octet -- GOOD: valid octal escape sequences SELECT E ' \\ 377' :: bytea ; -- decimal 255 SELECT E ' \\ 101' :: bytea ; -- 'A' character SELECT E ' \\ 000' :: bytea ; -- null byte 2. Using escape format strings with hex output format Since PostgreSQL 9.0, the default bytea_output is hex . Applications that mix hex -format output back into escape -format input processing can generate malformed escape sequences. -- Check current bytea output format SHOW bytea_output ; -- GOOD: Use hex format (recommended for all new projects) SELECT ' \x DEADBEEF' :: bytea ; SELECT ' \x 48656C6C6F' :: bytea ; -- 'Hello' -- GOOD: Use encode/decode for safe conversions SELECT encode ( ' \x DEADBEEF' :: bytea , 'hex' ); -- output as hex string SELECT encode ( ' \x DEADBEEF' :: bytea , 'base64' ); -- output as base64 SELECT decode ( 'deadbeef' , 'hex' ); -- hex string → bytea SELECT decode ( 'SGVsbG8=' , 'base64' ); -- base64 → bytea 3. Incorrect escaping during data migration or manual SQL When migrating binary data from other databases (Oracle, MySQL) or writing raw INSERT statements manually, developers often confuse octal and