AI 资讯

Engineering managers are almost entirely absent from the AI transformation discourse. There's a structural reason for that, and understanding it is the first step to doing something about it. Engineers write on the internet. C-suite decisions make headlines. Engineering managers absorb pressure from above, complexity from below, and produce outcomes that get credited in both directions. The system doesn't reward the EM voice publicly. But the EM position gives you something that's genuinely hard to replicate: accountability for what happens to the team, combined with proximity to all three layers of the problem at once. That's not a consolation prize. It's a specific kind of leverage, if you decide to use it deliberately. You're accountable for what nobody else fully sees Writers go where the audience is or where the authority sits. EMs are neither, which is why the playbooks keep missing them. Executives get advice that assumes frictionless implementation. Engineers get advice that assumes organizational stability. At the team level, neither holds. The EM isn't the only person with this view. A good Staff or Principal Engineer often has comparable exposure — technical depth, some business context, real influence on architecture decisions. In many organizations, the senior IC has more technical credibility than the EM and less organizational noise to cut through. The difference isn't the view. It's the accountability. When something goes wrong at the team level — delivery slips, quality degrades, an engineer burns out, AI adoption produces incidents instead of velocity — the EM is the one who carries it. That asymmetry is uncomfortable. It's also what makes the EM's perspective structurally different from everyone else's. You don't just see the intersection where the playbooks break down. You're responsible for what happens there. The question isn't whether that position is valuable. It is. The question is whether you're using it actively or just absorbing it quietl

A physiotherapist asked me a simple question a couple of months ago: "Can you build something to run my whole clinic?" So I did — solo, in about a week. Here's the full 2.5-minute walkthrough 👇 What PhysioFlow does PhysioFlow runs an entire physiotherapy clinic from one screen — built for India (₹, GST, WhatsApp, +91): Dashboard — attendance, collections & pending bookings at a glance Patient files — recharge session packs, track usage, auto-generate GST-ready bills Attendance in seconds with a QR scan Online bookings that convert straight into a patient file Reports — daily ledger, revenue, CSV/PDF export Patient portal — patients see their own sessions & prescriptions The stack Next.js + Supabase + TypeScript — multi-tenant, with row-level security so no clinic's data ever leaks to another. Try it Live now with a 14-day free trial, no card needed → https://physioflow.devfrend.com I'm Amar, a full-stack & AI engineer. I design and build products like this end-to-end. I'm open to work — SaaS builds, MCP servers, LLM apps & automation. Reach me on LinkedIn .

This is a submission for the Hermes Agent Challenge , under the Write About Hermes Agent prompt. I've spent the last week testing AI memory failure modes in a public evaluation harness. That work changed how I read agent memory systems. This is a writing submission, not a build submission. I did not build a Hermes Agent project for this challenge. I am writing from the perspective of someone testing how memory failures show up once agents can act. So when I look at Hermes Agent, the question I care about is not only: Can the agent remember useful things? The harder question is: When memory conflicts, which memory is allowed to govern the agent's action? That distinction matters. Hermes Agent is interesting because it is not just a chat interface. Its documentation describes an open-source agentic system with tool use, project context, persistent memory, skills, browser automation, checkpoints, delegation, scheduled tasks, and multiple memory providers. That is exactly the kind of system where memory stops being a convenience feature and starts becoming part of the agent's operating boundary. If an agent can run tools, edit files, browse, delegate work, schedule tasks, and remember across sessions, then memory is no longer just "context." Memory becomes governance. The Memory Problem I Would Watch For In a simple chatbot, bad memory is annoying. In an agent, bad memory can become operational. The failure mode is not only that the agent forgets something. Sometimes the more dangerous failure is that it remembers the wrong thing too confidently. A memory can be: relevant but stale, relevant but low-authority, relevant but superseded, relevant but only context, relevant but not allowed to determine the action. That is the distinction my own tests kept running into. Retrieval systems are usually good at answering: What memory is closest to the user's request? But safety often depends on a different question: What memory is allowed to decide what the agent should do? Thos

1. Why I use Kiro I've been using Kiro for almost 1 year now, I'm using it as a Cloud Architect and also to build side projects for fun. The main reason I use Kiro over other tools is how it works with you as an engineer. Over the months, I've noticed certain patterns in how I use Kiro. Let's go over them: Index 1. Why I use Kiro 2. Pair Programming with Kiro 3. Repeatable workflows as Skills 4. Using Plan, Specs and Agents 5. Council of agents 6. Documentation, Documentation, Documentation Final thoughts 2. Pair Programming with Kiro The most common way that I use Kiro is in Pair Programming. Pair Programming is when there are 2 developers working together on the same task, they can work in tandem or one of them can be the one guiding/planning while the other one does the code. In my case, with Kiro, I'm the one doing the guiding and planning while Kiro is the one executing and implementing the code. I'm also using Kiro as my rubber duck. If I have a new idea or I'm working on a blocking bug, I talk to Kiro so it can give me a different point of view, investigate and steer me into good practices. The main reason for me to do it this way is because once the session is over, I can run a prompt/skill to record everything from the session: Kiro, summarize this session and save it into a .memory folder with the format yyyymmdd and as a markdown So then everything that we've done is going to be recorded there. Do you remember everything that you've done yesterday? Maybe. But what about last week? And what about one month ago? I definitely don't remember it. In the classic Software Development Life Cycle, we have tickets, and we have a way that we can recall all this information, but the more detailed context of why you did it is going to be completely missed. Now, with tools like Kiro, this is possible to remember. You just have a .memory folder where you summarize all your sessions. So in the future, we could have a situation like this: Oh, I don't remember what changes

i'm trying to train an ASR model using the LibriSpeech recipe from SpeechBrain (without the language model) on a 100-hour dataset of dialectal Arabic speech. the model architecture uses a Conformer-small encoder and a Transformer decoder, with a total of around 13M parameters. the recipe uses a combination of two loss functions: CTC and KL divergence, specifically: 0.3 * CTC + 0.7 * KLDiv during training, both losses drop significantly during the first few weight updates, but then quickly plateau. the CTC loss gets stuck fluctuating around the 60-80 range, while the KL divergence loss remains around the 60s as well for the rest of training. as a result, the model does not converge properly, and the validation WER stays close to 100%. i’ve already tried several things: adjusting the learning rate, changing the number of warmup steps, modifying the number of epochs, tuning the batch size and reducing the vocabulary size from the default 5000 to 1000. none of these changes seem to help. the training dataset is not publicly available and is weakly labeled. the validation and test sets come from the MGB2 dataset. at this point, i genuinely don’t know what the root cause might be. i’ve experimented with many different approaches, but the model still refuses to converge. has anyone encountered a similar issue where their model gets stuck early in training and never improves? if so, what ended up being the cause or solution? any feedback, suggestions, or ideas would be greatly appreciated. submitted by /u/Sweet-Hamster-4991 [link] [留言]

Every time I sit down to learn something, I find myself trapped in the same cycle. I start with a tutorial. Halfway through, someone says there's a better tutorial. I switch. Then I discover a book that supposedly explains the topic better than the tutorial. Then a YouTube video claims the book is outdated. Then a developer on social media recommends an entirely different resource. Before I know it, I've spent three hours researching how to learn instead of actually learning. Does the information overload stop or will there always be another resource, another course, another book, another video, another roadmap, another expert with a different opinion. The internet has made knowledge abundant, but abundance creates a paradox of choice. One person says to learn JavaScript from documentation, another says build projects immediately, another recommends a paid course, someone else insists that free resources are better. Every recommendation sounds convincing. Every path seems important. The result is paralysis. Instead of moving forward, I keep searching, instead of building I keep comparing, instead of learning I keep consuming. finished teaches more than a hundred bookmarked tutorials. At some point, every learner must accept a difficult truth that the goal is not to find the best resource it is to become better. Those are not the same thing. A person can spend months researching the perfect learning path and never write a meaningful line of code while another person can pick a decent resource, make mistakes, build projects, and improve every day. The second person wins not because they found better information but because they used the information they already had. I've started realizing that learning is a lot like fitness. At some point, reading about exercise becomes a form of avoiding exercise. The same thing happens in programming. Reading about coding becomes a way to avoid coding. Researching becomes a substitute for practice. The search for the perfect resourc

Environmental activist Erin Brockovich has a new mission.

Overview I built Hermes Financial Agent — an AI-powered financial assistant for investors in the Egyptian Exchange (EGX). The goal: not just calculate portfolio value, but explain risks in a transparent, auditable way. Features Real EGX market data via Yahoo Finance Portfolio tracking and valuation Daily financial reports Telegram-user portfolio isolation Cached quote fallback for resilience Explainable risk insights Explainable Risk Intelligence Unlike traditional trackers, Hermes surfaces: Portfolio concentration risk Stale market data exposure Quote coverage percentage Valuation gaps from unavailable data Users understand confidence level behind their valuation — not just the number. Technical Stack Hermes Agent framework Python GitHub Actions (automated smoke testing) Offline-safe quote fallback architecture Repository 🔗 GitHub Repository Future Roadmap News sentiment analysis Investment thesis tracking Market briefing generation Advanced financial reasoning agents hermeschallenge

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

The premium gaming handheld's price is expected to be around $1,500.

NVIDIA's CEO Jensen Huang is set to take the stage for his GTC Taipei keynote at 8PM PT / 11PM ET. You can watch all the announcements here and embedded below. Rumors have been flying about what to expect from today's presentation, but the big one is the possibility of a partnership with Microsoft and […]

submitted by /u/Raman606surrey [link] [留言]

Hey guys, would love to get some feedback on whether my approach here makes sense. I’m building a real-time chat application where users can upload and receive images/files. Files can be fairly large (up to ~100MB). Current stack is: fastapi, websockets, tanstack, postgres and redis. I use GCP as my cloud provider. My current flow is: Backend generates a signed URL Frontend uploads directly to a GCS bucket A Cloud Function handles post-upload processing For downloads, the frontend fetches directly from GCS using redirects/signed URLs so the backend doesn’t become a bottleneck This architecture works great for smaller files (<30MB), but once I started testing larger uploads (100MB images/videos), I noticed very high memory consumption during processing (btw pagination & virtualization is used throughout the project). I’m trying to figure out what’s considered best practice here for large media uploads in chat systems: Should compression/downscaling happen client-side or server-side (currently there is not compression at all)? Also, Is it common to generate thumbnails/previews (for images) separately while keeping the original untouched? Should I stream uploads instead of buffering them? Are Cloud Functions even the right choice for heavy file processing? For images specifically, I’m considering: client-side compression before upload, automatic thumbnail generation, storing multiple resolutions, converting to formats like WebP/AVIF. Would love to hear how you guys handle this in production systems. Thanks! submitted by /u/omry8880 [link] [留言]

The A Minecraft Movie sequel officially has a title: A Minecraft Movie Squared. What's more, we now know that Kirsten Dunst will star as Alex, the game's female character option, and that Matt Berry is set to play an even bigger role in this film. He voiced Nitwit in the first movie, but in this […]

submitted by /u/InvestigatorSoft5764 [link] [留言]