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A technology reporter for the New York Times, named Stuart Thompson sold his house for $605,000 — without a real estate agent, and without losing a dime of commission. submitted by /u/RaspberryOk1888 [link] [留言]

The crypto weed vape found me on 4/20, the high holiday of cannabis enthusiasts everywhere. It arrived over Slack with the thumbnail of a man exhaling a plume of vapor, the words "every hit delivers Bitcoin" emblazoned across it. It claimed to be advertising a device called Gudtrip, and I thought everything about it sounded […]

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]

Maybe it’s just me, but I’ve been running into more and more half-working products lately. Buttons that do nothing. Checkouts that fail silently. Forms that throw errors with no explanation. And not from random small sites either, from companies that should absolutely know better. I think it’s the result of AI + fast shipping + less quality control. Teams are pushing out features at a speed that wasn’t possible 2 years ago, but the QA, testing, and ownership of quality hasn’t scaled with it. AI didn’t break the web. It just made it easier to ship things that were never properly checked. The other thing I’ve noticed: when something breaks now, you can’t even get to a real person. Support bots loop you in circles, and the actual humans who could fix it are buried somewhere behind 5 layers of auto-responses. Curious if others are seeing the same thing, or if I’m just unlucky lately. submitted by /u/Good-Locksmith-4978 [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

I’ve spent a lot of time lately thinking about why some LLM agents feel "intelligent" while others just feel like chatbots with a slightly better prompt. It almost always comes down to how the system handles memory. When we treat the context window as the only place for state, we hit a ceiling very quickly. To build an actual agent, we have to move away from "one big prompt" and toward a layered memory architecture. Agentic Memory can be categorized in 4 layers by their function: Working Memory: The current context window. It's our RAM—fast, essential, but wiped clean after every session. Semantic Memory: The Vector DB or knowledge base. This is where the "world rules" and global conventions live. It’s the reference manual the agent checks to stay aligned. Procedural Memory: The "how-to" layer. Instead of stuffing every tool description into the prompt, the agent maintains a lean index of skills and pulls in the full implementation only when a specific task triggers it. This keeps the context window clean. Episodic Memory: This is the hardest part. It's the ability to distill a past interaction into a reusable insight. The real engineering challenge here isn't storage—it's the "forgetting" logic. Deciding what is noise and what is a core pattern is where most frameworks still struggle. Depending on the use case, the architecture changes: Reflex Agents: Just Working Memory. Support Agents: Working + Procedural. Coding Agents: The full stack. The gap between a demo and a production-ready agent is usually the distance between simple RAG and a functioning episodic memory. The ability to compress experience into a usable state is still a significant hurdle. Which of these layers are you currently implementing, and how are you handling the "forgetting" logic in your episodic memory?