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Selling server infrastructure would put Meta in competition with Amazon and Google.

An optimal ratio of 10-15 grams of larvae per gram of specimen minimized cleaning time with no bone damage.

SpaceX reportedly showed investors a "handset-like" AI device before going public. It could be another signal SpaceX wants to expand into wireless.

Vendor onboarding usually starts with one clean request and then turns into a messy thread. Procurement asks for a W-9, security asks for a SOC 2 report, finance asks for remittance details, legal asks for an executed agreement, and the vendor replies with four attachments across three messages because different people own different parts of the process. That is exactly the kind of workflow where a generic "AI email assistant" gets risky. You do not want a model improvising legal language, requesting bank details in the wrong channel, or forwarding a confidential report to the wrong internal alias. You want the agent to own the repetitive coordination while your application keeps the state machine, policy, audit log, and approvals. The pattern I reach for is a dedicated Nylas Agent Account: vendors@yourcompany.com . It is a real mailbox the onboarding agent owns. It receives the vendor's replies, detects what is attached, updates your vendor record, sends safe reminders, and escalates missing or sensitive items to a human. The agent is not borrowing an employee's inbox, and it is not scraping a shared procurement mailbox. It has a grant, an email address, webhooks, threads, folders, and the same Messages API you would use for any other mailbox. I work on the Nylas CLI, so the terminal examples below use the commands I would use while building and debugging this flow. I also include the raw API calls because the production version belongs in your service, not in a shell script. What the agent should own Start by drawing the boundary tightly. A vendor onboarding agent should own message handling and coordination, not business approval. Good responsibilities: Receive vendor replies at a stable address. Read message bodies and attachment metadata. Match a reply to an existing vendor record. Detect which onboarding items are complete, missing, expired, or unreadable. Draft reminders and status updates. Schedule handoff calls when the vendor asks for help. Escalate sensit

Hi everyone. Do the ACL arr may 2026 reviews come out of July 2nd or do they come out on July 7 th?? How much does one need to get into Main or Findings? I am a bit new to this. Thanks a lot folks. submitted by /u/Anshuman3480 [link] [留言]

Building an AI agent is fun. Fixing its production latency when it's juggling live data, RAG, and text-to-speech? Not so fun. In the latest episode of the AI Agent Clinic, we sat down with developer Sami Maghnaoui to debug PlaybackIQ, a football / soccer agent he built to provide pre and post match analysis with text to voice, and minute-by-minute match insights with interactive UI. The app was awesome, but under heavy "match day" data loads, the wait times were killing the UX. Here’s how we fixed it: The Bottleneck: We implemented OpenTelemetry on the Agent Platform to trace exactly where the LLM calls and data retrieval were hanging up. The Scale: We shifted the deployment to Cloud Run to properly handle concurrent traffic. The Result: We managed to slash the agent's latency by 80%. If you're dealing with sluggish LLM response times in your own apps and want to see what a production-grade fix looks like, we recorded the whole teardown and rebuild. 🎥 Watch the teardown here: [ https://youtu.be/G7olcqETSn8 ] (Let me know in the comments what your go-to stack is for tracing LLM latency!)

The problem no one was solving Every Algerian developer building with AI hits the same wall: an international payment card. OpenAI, Anthropic, Google — every major AI provider assumes you have one. Most Algerian developers don't, or don't want to deal with the friction of currency conversion, card rejections, and unpredictable billing in a foreign currency. That's not a minor inconvenience. It's a barrier that quietly excludes an entire generation of developers from building with the best AI models available — not because they lack the skill, but because of infrastructure that was never designed with them in mind. The vision: AI sovereignty, not just AI access Access alone isn't the goal. The goal is sovereignty — Algeria having its own AI infrastructure layer, controlled locally, billed locally, and built to local compliance standards, instead of depending entirely on foreign gateways with no local accountability. That's what DEVUP AI is: Algeria's first AI inference gateway, built from the ground up to remove every friction point between an Algerian developer and the AI models they need. What DEVUP AI actually does 170+ AI models — including DeepSeek V4, Llama 3.1 405B, Qwen 3, Gemma 2, Mistral, GPT, Claude, and Gemini — through a single API OpenAI-compatible and Anthropic-compatible — point your existing SDK at our endpoint, no code rewrite needed Local DZD billing via Edahabia/CIB — no international card required SATIM-certified payment infrastructure — full compliance with Algeria's national payment standards Scoped JWT authentication for production-grade security A dedicated SDK ( npm install devupai ) and full documentation, so integration takes minutes, not days The technical bar was non-negotiable: this had to be production-grade from day one, not a side project. SATIM certification alone meant building proper transaction validation, receipt generation, chargeback tracking, and rejection-rate monitoring — the same rigor a bank would expect from a payment pr

“We will own nothing, it's truly sad.”

I wrote a detailed comparison of GitHub Actions vs Jenkins after working with both while learning DevOps. Instead of focusing on "which one is better," I looked at questions like: When does GitHub Actions become enough? When is Jenkins still the right choice? Performance and scalability Security and maintenance Cost Real-world use cases I'd love to hear where you disagree or what your production experience has been. Article: actions vs jenkins submitted by /u/ganeshak11 [link] [留言]

Every data grid demo looks incredible with twenty rows. The columns line up. The hover state is tasteful. The checkbox has confidence. Someone scrolls three inches and everyone quietly agrees that software has advanced. Then the real product arrives. Fifty thousand rows. Twenty columns. Editable money. A custom status cell. Filters. Sorting. Horizontal scrolling. A user who pastes something suspicious from Excel. A product manager asking whether the total row can stay pinned while the server is slow. That is when a table stops being a table and starts becoming infrastructure. So we built a benchmark. Not a perfect benchmark. Those do not exist. A useful one. What we measured The fixture is intentionally boring: 50,000 deterministic rows 20 fixed-width columns 1,200 by 600 pixel viewport two editable columns sorting filtering virtual scrolling production bundles fresh browser contexts raw samples committed to GitHub No network requests. No paid-only feature tricks. No images. No grouping. No heroic demo code designed to make one library look blessed by destiny. The report measures: JS gzip : reachable JavaScript after gzip Ready median : navigation until the grid adapter mounts and two animation frames pass Scroll settle : one scripted vertical and horizontal jump plus animation frames Mounted cells : body cells in the DOM after the scroll Interaction health : heap, long tasks, estimated FPS, dropped frames Live benchmark: https://vitashev.github.io/react-data-grid-benchmark/ Source and raw samples: https://github.com/Vitashev/react-data-grid-benchmark The part most benchmarks get wrong Not every grid exposes the same surface. For example, MUI X Data Grid Community uses 100-row pagination for this workload. That is a valid product boundary, but it is not the same as continuously virtualizing 50,000 rows. So the ranked tables include only compatible continuous-scroll libraries. MUI remains in the fixture and raw data, but not in the leaderboard. That makes the benchma

I’ve always thought building a mobile app required climbing a massive learning curve just to get a basic environment set up. To test that theory, I tried building my very first Android app using Google AI Studio . Five minutes later, I had a working prototype. The coolest part about this isn't just the speed: it’s that anyone can do this. The traditional barriers to building software are disappearing, making it incredibly easy to just start creating. I recorded the whole 5-minute process here if you want to see what it looks like in practice: What's in the video Prompting AI Studio to build a native Android app from scratch Progressive Webapp (PWA) vs Android Native App in 2026: feature comparison Sideloading the app onto an Android device via USB-C cable. No Play Store required What happens when the AI gets something wrong? Fixing bugs in a vibe coded app

The AI neocloud provider, which specializes in hosting open source models, last raised at a $3.3 billion valuation in early 2025.

The nine-year-old scooter and bike-share company has said it needs the funds to help pay down around $1 billion in liabilities.

We started using Jira to manage our internal development workflow. At first it worked fine, but once we outgrew the free tier, the cost became hard to justify. At $15 per user per month, we were suddenly looking at a bill that did not match how we actually used the product. What we Built We created WannaTrack, a lightweight project management tool designed for small dev teams that do not need enterprise complexity. The goal was not to recreate Jira. It was to remove everything we did not use. Key ideas : minimal agile board with no clutter or heavy configuration simple issue tracking flow fast interface for daily development work minimal setup and no onboarding overhead Migration from Jira One of the biggest concerns was switching tools without breaking our workflow. So we built a Jira import tool that lets you migrate existing tickets into WannaTrack without manual effort. This allowed us to switch internally without downtime. Where it is now We now use WannaTrack daily for our own development workflow and are opening it up to other teams who feel the same pain with traditional tools. If you are a small dev team, indie hacker, or startup looking for a simpler issue tracker without overhead, you can check it out here: https://wannatrack.com

If you've ever tried to build a simple command/telemetry protocol between a PC and a fleet of SDR receivers, sensors, or embedded devices, you know the usual options aren't great: Roll your own binary format — fast, but you end up writing and maintaining custom serialization code for every device type, and debugging mismatched structs across machines is painful. Protobuf / FlatBuffers — robust, but require you to define your message layout in a schema file upfront, run a code generator as part of your build, and commit to a fixed structure. Adding a new device type or a new parameter means editing the schema, regenerating, recompiling everything. JSON over the wire — easy to debug, but heavy for anything real-time or bandwidth-constrained. I ran into this while working on a multi-SDR receiver system and ended up writing MessageFrame — a small C++17 library that lets you build structured messages dynamically, without any schema files or code generation. The basic idea Instead of defining a struct for each device type, you address each parameter with two strings — a device name and a parameter name — and the library handles the rest: // One message, multiple devices, assembled at runtime msgframe :: MessageFrame msg ( MSG_TELEMETRY , TYPE_PERIODIC , src = 1 , tgt = 2 ); msg . add ( "sdr_1" , "rx_gain" , VALUE ( 30.0 )); msg . add ( "sdr_1" , "center_freq" , VALUE ( 915'000'000.0 )); msg . add ( "sdr_1" , "sample_rate" , VALUE ( 2'000'000.0 )); msg . add ( "sdr_2" , "rx_gain" , VALUE ( 25.0 )); msg . add ( "sdr_2" , "lock_status" , VALUE ( true )); msg . add ( "psu_1" , "voltage" , VALUE ( 12.04 )); msg . add ( "psu_1" , "temp_c" , VALUE ( 47.3 )); // Attach raw IQ data alongside the parameters std :: vector < uint8_t > iq_buffer = { 0x01 , 0x02 , 0x03 , 0x04 }; msg . add_attachment ( "raw_iq" , std :: move ( iq_buffer )); // Serialize into a buffer, send over whatever transport you use std :: vector < uint8_t > out ; msg . serialize ( out ); send_udp ( out . data (),

submitted by /u/Dear-Economics-315 [link] [留言]

Cloudflare is giving AI companies until September 15 to separate web crawlers used for search from those used for AI training and agents, or risk being blocked by default on many publisher sites.

After a lengthy legal dispute, Krafton has settled with its subsidiary Unknown Worlds Entertainment, which is developing Subnautica 2, and will pay bonuses to the studio's staff, Bloomberg reports. The dispute began last year after Krafton pushed out Unknown Worlds' cofounders, Charlie Cleveland and Max McGuire, and its CEO, Ted Gill, ahead of a potential […]

Microsoft will likely soon follow Sony and stop the production of physical discs for Xbox games. But instead of leaving physical discs behind entirely, sources familiar with Microsoft's plans tell me the company has quietly been working on a disc-to-digital feature that will allow Xbox owners to digitize their existing physical game collections. Xbox employees […]

Is PlayStation about to make the same mistake Microsoft made with Xbox One?