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Author here. Wrote this from the vantage point of having shipped Android apps from 2010 to 2019-20. It's a platform-war retrospective along an axis I don't see articulated cleanly very often: update friction and channel control rather than runtime capability. Short version of the argument: The native-only residue (where mobile genuinely wins on capability) is thinner than the narrative claims. UPI in India because the device is the channel. Frame-budget and AR-heavy games. Sustained background GPS. RAW camera. Delivery/on-demand, with a tell (the apps are richer than the web versions because that's where the channel control is, not because the web cannot do it). Electron is the keystone, not a defensive aside. Slack, VS Code, Postman, Bruno, Spotify desktop. If web-versus-native were the deciding axis, the entire web-shell desktop category should have failed. It did not, because the desktop channel is open and the maintainer ships on their own cadence. PWAs are the reverse proof. Apple controls three brakes on the iOS web channel: the WebKit-only rule, the buried Add to Home Screen, and the notification permission. iOS web push did not land until 16.4 in March 2023, years after Android. When the channel is suppressed, the maintainer's update advantage does not save you. The Android hobbyist economy died not from a market outcome but from a channel outcome. Cambridge Analytica 2018 was the public license for a multi-year platform-hardening cycle (target-SDK floor, scoped storage, Play Integrity, foreground-service mandates) that progressively re-priced what kind of software was even shippable. The store does not just control the install button; the channel itself keeps narrowing on the people inside it. It's Part 1 of a two-part series on delivery channels. Posting because I'd rather have the argument tested here than not. submitted by /u/lordVader1138 [link] [留言]

My eyes have seen the PC gaming promised land, and it's a beautifully bright world without a shred of blurriness. It's warm, it looks lovely, and it's impeccably sharp. Also, it's expensive as hell. I've dipped my toe in this world by testing a pre-production version of the upcoming Asus ROG Strix Scar 18, which […]

IO Interactive’s 007 First Light is here, and it’s just as stunning a James Bond mov — err, video game — as we hoped it would be. Pardon the confusion, the title’s engaging tutorial really feels like you’re watching a great Bond movie at times. Whether you’re a longtime Hitman fan who’s been eagerly waiting […]

Earlier this month, I finally achieved the elusive goal I had set for myself in Bungie's Marathon. I collected six of the game's rarest items, allowing me to attempt and then successfully clear the raid-style Compiler boss. I felt a massive weight lift off my shoulders - nearly 185 hours of playtime and I had […]

The Enhanced Games — a singular sporting competition where a majority of the athletes were on performance enhancing drugs — may herald a new business model that the tech industry is ready to embrace.

There is a huge gap in open source AI accelerators, so I implemented mine . Popular and well known ones are already legacy and doesn't support contemporary operations like Attention. Here is what makes mine special: Attention mechanism smelted directly into silicon Prototyped end-to-end on FPGA (AWS F2) Benchmarked against PyTorch -based workloads Built on the RocketChip architecture (RISC-V) Native BF16 support Up to 225× speedup on vanilla attention mechanism Up to 96× speedup on TinyBERT Up to 50× speedup on ViT Up to 30× speedup on GPT-2 prefill I would really appreciate it if you check the repo and give me feedback! submitted by /u/Barrnie [link] [留言]

First time, so no idea. submitted by /u/camelCasedUser [link] [留言]

As part of my game development journey, I recently created Hidden Collector , a Unity-based game where players explore levels and collect hidden items while progressing through different challenges. This project started as a way for me to improve my Unity and C# skills, but it quickly became an opportunity to learn about game design, UI systems, audio management, scene transitions, and player experience. What I Worked On While building Hidden Collector, I implemented: Player movement and interactions Collectible item systems Multiple game levels UI menus and game screens Audio and sound effects Progress tracking Game flow and scene management Challenges During Development One of the biggest challenges was making different game systems work together smoothly. Something as simple as collecting an item often required updates to UI elements, game state management, and progression systems. Debugging these interactions taught me a lot about organizing Unity projects and writing maintainable code. What I Learned This project helped me gain experience with: Unity Engine C# scripting Game architecture UI implementation Audio management Debugging and testing Most importantly, I learned that building complete projects teaches far more than following tutorials. Play the Game You can try Hidden Collector here: https://sinxcos07.itch.io/hiddencollector Screenshots What's Next? I'm continuing to improve my game development skills by building new projects, experimenting with different mechanics, and learning more about creating engaging player experiences. If you try the game, I'd love to hear your feedback. By Suryansh Sinha (sinxcos07) Connect With Me GitHub: https://github.com/sinxcos07 LinkedIn: https://www.linkedin.com/in/suryansh-sinha/ Play Hidden Collector: https://sinxcos07.itch.io/hiddencollector

Now that almost everyone has thought about or is actively integrating AI workflows into their projects, some might ask is this all worth the cost? Many think the current economics of the AI space don't scale and that there will be upward price movement. Others still might not be comfortable with sending their data to remote services for processing. Then there is the crowd that wants to deploy models in small spaces with limited compute. Are there ways we can deploy small models locally and run at a lower cost? Yes with Knowledge Distillation . Knowledge distillation can get a bad rap due to it's questionable use in training some Large Language Models (LLMs). But it's a perfectly valid way to transfer performance from a larger model to a smaller one. Especially when both models are yours and/or open. This article will explore progressive distillation which is a technique to incrementally transfer knowledge from a series of larger teacher models into a smaller student. Install dependencies Install txtai and all dependencies. pip install txtai [ pipeline - train ] datasets Setup the Training Pipeline The first step we need to do is setup up the training pipeline. We'll use the Hugging Face Training framework to build a series of models. The following code establishes a train method, test method and loads the classification training data. from datasets import load_dataset from transformers import AutoModelForSequenceClassification , AutoTokenizer from txtai.pipeline import HFTrainer , Labels def train ( teacher , student , distillation , ** kwargs ): trainer = HFTrainer () model = AutoModelForSequenceClassification . from_pretrained ( student , trust_remote_code = True ) tokenizer = AutoTokenizer . from_pretrained ( student , trust_remote_code = True ) return trainer ( ( model , tokenizer ), ds [ " train " ], columns = ( " sentence " , " label " ), maxlength = maxlength , teacher = teacher , distillation = distillation , ** kwargs ) def test ( model ): labels = Labels (

The Theorem That Changed How We Think About Databases In 2000, Eric Brewer stood at a conference and proposed a conjecture that would reshape distributed systems forever: "You can only guarantee two of these three properties at the same time: Consistency, Availability, and Partition Tolerance." Two years later, Seth Gilbert and Nancy Lynch proved it mathematically. It became known as the CAP Theorem — and every distributed system architect since has had to wrestle with it. It sounds abstract. But once you understand it, you'll never look at a database choice the same way again. You'll understand why Amazon DynamoDB and Google Spanner make opposite architectural choices. You'll know why your bank uses PostgreSQL while Twitter uses Cassandra. Let's break it down from first principles. The Three Properties C — Consistency Every read receives the most recent write, or an error. There's only one version of the truth — all nodes agree. Not the same consistency as ACID . CAP consistency (linearizability) means every read reflects the latest write across all nodes. ACID consistency means transactions don't violate database constraints. Different concepts, same confusing word. A — Availability Every request receives a non-error response — though it might not be the most recent data. The system is always up and answering. Note: "Available" in CAP doesn't mean "fast." It means "responds without error." A system that always returns a (possibly stale) answer is Available. P — Partition Tolerance The system continues operating even when network messages between nodes are lost or delayed. A partition is when part of your distributed system can't communicate with another part. The Unavoidable Truth: P Is Not Optional Here's the insight that makes CAP actually useful: In any real distributed system, partitions will happen. Networks fail. Cables get cut. Data centers lose connectivity. AWS regions go down. Since you must tolerate partitions (or have a single-server system, which does

By Suryansh Sinha (sinxcos07) Introduction Recently, I built AstroFit , a fitness-focused web application as a personal project to learn more about modern web development, deployment, databases, and building complete applications from idea to production. This project helped me understand how different parts of a web application work together, from the user interface to backend functionality and deployment. Why I Built AstroFit I wanted to work on a project that felt practical and useful while also helping me improve my development skills. Instead of creating a simple clone project, I decided to build a fitness application where I could experiment with real-world features and deployment workflows. Development Journey Building AstroFit involved much more than just creating pages and connecting them together. Some of the areas I explored while working on this project included: Frontend development Backend integration Database management Authentication systems Deployment and hosting Debugging production issues One of the biggest learning experiences was understanding how different technologies communicate with each other in a complete application. Future Plans I plan to continue improving AstroFit by adding more features, refining the user experience, and expanding its capabilities over time. This project is still evolving, and I'm excited to keep working on it. Project Links Live Demo: astrofit-fitness.vercel.app GitHub: sinxcos07 / astrofit-frontend Fitness platform combining workout tracking and astrology-inspired personalization. AstroFit AstroFit is a modern fitness web application that combines workout tracking with astrology-inspired personalization to create a unique and engaging fitness experience. Features Modern responsive UI Astrology-inspired fitness experience Workout tracking interface User authentication system Backend integration Smooth and interactive design Mobile-friendly layout Tech Stack Frontend HTML5 CSS3 JavaScript Backend Node.js Express.js SQL

"A language model that answers questions is a tool. A language model that decides which questions to ask and then acts on the answers is something else entirely." Introduction: When Models Started Deciding For the first several years of modern NLP, the task was always the same: given input, produce output. One forward pass. One completion. Done. In 2022, a paper from Google Brain asked a different question. What if, instead of producing an answer directly, a model could reason about what information it needs, act to retrieve it, and revise its thinking based on what it found? The paper was ReAct: Synergizing Reasoning and Acting in Language Models (Yao et al., 2022). Applying it to an LLM created something qualitatively different: a model that could take real-world actions and adapt its reasoning based on what came back. A completion model is a calculator. An agent is a process: it has a goal, takes steps toward it, and updates when things go wrong. This week I went deep on the architecture behind these systems, the frameworks that define them, and what the open problems look like from a research perspective. Part 1: What Makes a System "Agentic"? The word "agent" gets used loosely in current literature. A clean definition comes from Russell and Norvig's Artificial Intelligence: A Modern Approach : An agent is anything that perceives its environment through sensors and acts upon that environment through actuators. For an LLM-based system, this is a loop: perceive an observation, reason about what to do, act via a tool call or output, observe the result, and loop again. But not every loop qualifies as agentic. Three properties distinguish genuinely agentic systems from tool-augmented chatbots: Property What It Means Goal persistence Maintains the original goal across multiple steps without re-prompting Adaptive planning Revises its approach based on intermediate results Tool autonomy Decides when and which tools to use, not just how to use one it was told to call Mos

This is a submission for the Hermes Agent Challenge : Write About Hermes Agent What if the biggest limitation in AI today isn't reasoning, model size, or context windows? What if it's memory? Every morning, millions of people open ChatGPT, Claude, Gemini, or another AI assistant and start a conversation. The AI seems intelligent. It writes code. It explains concepts. It helps brainstorm ideas. It can even help design an entire software architecture. Then the conversation ends. Tomorrow? It remembers nothing. Imagine hiring a senior engineer who forgets everything at the end of every workday. Every morning you would need to explain: What your company does How your product works Which technologies you use Why certain decisions were made What happened yesterday Nobody would call that employee productive. Yet this is exactly how most AI systems operate. And it reveals something important: Most AI agents aren't actually learning from experience. They're simply reasoning over whatever context happens to be available right now. That distinction may define the future of agentic AI. Because the next generation of AI won't just need better reasoning. It will need memory. And that's where Hermes Agent becomes interesting. The Strange Reality of Modern AI The public perception of AI often looks like this: User → AI → Intelligence But the reality is closer to this: User → Context Window → AI → Response The AI only knows what exists inside its current context. Once that context disappears, so does most of its understanding. This is why many AI experiences feel surprisingly repetitive. You spend 30 minutes explaining your project. The AI finally understands your goals. The answers become better. The recommendations become more relevant. Then the session ends. The next conversation starts from scratch. Not because the model isn't powerful. But because the knowledge never became persistent. Context Windows Are Not Memory A context window is not memory. It is temporary working space.

This is a submission for the Hermes Agent Challenge : Write About Hermes Agent Most AI agents have a dirty secret: they forget everything the moment the session ends. You explain your project once. Then again next time. And again. The agent never gets better at your workflow — it just stays a general-purpose tool that happens to be smart. Hermes Agent is built differently. It ships with two systems that together form something closer to a genuine long-term memory: a Skills System and a Persistent Memory layer. This post digs into how they actually work — not the marketing summary, but the mechanics. The Problem With Stateless Agents Before getting into Hermes, it's worth understanding what problem this solves. Standard LLM-based agents operate inside a context window. Everything the agent knows during a session lives in that window. When the session ends, it's gone. The next time you open a conversation, you're talking to an agent with no memory of you, your codebase, your preferences, or the workflows you've developed together. Some tools patch this with naive "memory" — they dump a text blob of past conversations into the system prompt. This works up to a point, but it's not selective, it gets expensive as context grows, and it doesn't help the agent get better at tasks — just recall facts. Hermes takes a different approach with two distinct systems serving different purposes. System 1: The Skills System (Procedural Memory) Skills in Hermes aren't plugins you install. They're on-demand knowledge documents — markdown files the agent loads when it needs them, and more importantly, creates on its own when it discovers something worth remembering. The SKILL.md Format Every skill is a structured markdown file with a YAML frontmatter header: --- name : deploy-runbook description : Our deployment runbook — services, rollback, Slack channels version : 1.0.0 metadata : hermes : tags : [ deployment , runbook , internal ] requires_toolsets : [ terminal ] --- # Deploy Runbook

I went through a bunch of older posts here about research vs dev roles, but most of them were either very general or not really in a similar situation, so posting this. I’m a final year ISE student from a tier-3 college. Over the past 1.5–2 years I’ve been focusing quite a bit on ML research instead of just the usual DSA + dev route. Current situation: 1 paper in EMSE 1 in JAIR 1 under production still but hopefully going to a Q1 journal 1 I’m trying for NeurIPS main track (I know this one’s a long shot) -> Already under review and didn't get bench rejected 2 month internship at Accenture in 3rd year Some ML projects apart from the research work I know not everything will land. But assuming a realistic outcome where maybe 1–2 of these get accepted at a decent level (Q1/A* types), I’m trying to figure out what that actually changes. A few things I’m confused about: For jobs in India: Does this actually help with shortlisting for ML/SDE roles, or after a point does it not matter much and it just comes down to DSA + interviews anyway? Also, being from a tier-3 college, does this help offset that at all? Or do companies still filter heavily based on college first ? I've seen a lot of people from IIT/NIT get into research/MLE roles in big MNC's rather than preferring those in Tier-3 colleges. For higher studies: Does having papers like this make a noticeable difference for PhD abroad (US/EU), or is it just a “nice to have”? Do colleges really care about the difference between something like NeurIPS vs a Q1 journal vs IEEE Access, or is it all seen more or less similarly? And finally, I'm planning to do my M.tech in India itself by writing GATE 2027, do you recon the value of these paper will actually help me in these colleges (say IIT's/IISC/NIT ?) And one thing I’m seriously unsure about: If I’m leaning towards industry (ML/AI roles), is continuing research actually worth the time, or would that effort be better spent on DSA, systems, etc? Also, is it even realistic to

This is The Stepback, a weekly newsletter breaking down one essential story from the tech world. For more news about gadgets and smartphones, follow Dominic Preston. The Stepback arrives in our subscribers' inboxes at 8AM ET. Opt in for The Stepback here. How it started In 2023, the European Union agreed on two landmark pieces […]

https://preview.redd.it/llqlupnwng4h1.png?width=2188&format=png&auto=webp&s=7fae5860babaffa1c8bfdcb1468b374eb38ac55d I'm currently building a computer vision application. I've managed to successfully train a YOLO model to detect the object I'm interested in for my videos. The image above shows some visualisations of the YOLO model outputs for some of the videos. I want to essentially cluster these strands in the image into groups based on their separation distance and return a string telling me the number of strands in each group from left to right (e.g. 1-2-3). The target value for each column in the image (where each column corresponds to a video) is 1-2-3, 1-2-3-2-3, 1-1-2-3-3-3-3 and don't worry about the fourth column for now 😄. The rows show the x vs t, y vs t and x vs y vs t for all the detections and the points are sized based on the detection box area. In the fourth column I have some background object detections which I want to ignore hence why I've also visualised detection box area. I've managed to train a XGBoost classification model that gives 70ish% accuracy however Bayes error is making me think I should be able to do much better than this. How would you approach trying to predict these strand clusterings? Some extra info that might help; there are at max 8 groups and each group can have only at max 3 strands. submitted by /u/mitbull420 [link] [留言]

Is it a hidden gem, a cult classic, or hopelessly dumb? We vote "all of the above."

I'm integrating TikTok into my SaaS platform and I'm having a hard time figuring out which API I actually need. My goal is: - A TikTok user logs into my platform using TikTok OAuth. - I obtain an access token. - Using that token, I want to send and receive TikTok messages directly from my platform (similar to how platforms like Sambad.io, ManyChat, etc. work). The problem is that TikTok's API documentation feels pretty vague regarding messaging. I can find information about Login Kit, Content Posting APIs, Creator APIs, and Business APIs, but I can't clearly determine whether TikTok provides public APIs for sending and receiving direct messages. While researching, I noticed that Sambad.io appears to support TikTok messaging, which makes me wonder if they're using a partner-only API or if they have some special TikTok partnership. Has anyone implemented TikTok messaging integration before? Specifically: - Does TikTok provide public APIs for sending and receiving DMs? - Are these APIs restricted to approved partners? - Which TikTok product/API should I be looking at? - If messaging APIs are partner-only, what is the process for getting access? Any guidance or real-world experience would be greatly appreciated. submitted by /u/AffectionateTouch103 [link] [留言]

Despite headwinds from the current administration, automakers continue to release well-equipped EVs with bigger battery packs and increasingly faster charging speeds. For those who want to travel further between plugging in, the future is still bright, just slightly tinted. But there haven't been many sedans starting around or below $50,000, as crossover SUVs have largely […]