Hey HN I've been using ECS for a while now and found it annoying having to log into the console everytime I use Lens for Kubernetes but couldnt find an equivalent for ECS so i built one! The project is open source as well https://github.com/utibeabasi6/mercek

Can an AI decide that it's going to nefariously drive the human it's interacting with slowly insane? submitted by /u/Numerous-Cup1863 [link] [留言]

The Framing Problem When industry analysts discuss "AI CAD," they are frequently conflating two fundamentally different computational paradigms: generative mesh synthesis and parametric feature modeling. This conflation has produced a decade of inflated expectations, underwhelming demos, and a persistent belief that real AI CAD is still "coming." It is not coming. For a specific and technically meaningful definition of AI CAD, it has arrived. Mesh Generation vs. Parametric Modeling: Why the Distinction Is Everything Contemporary generative 3D tools including neural radiance field reconstructions, diffusion-based mesh generators, and implicit surface networks produce geometry as an unstructured point cloud or polygon mesh. These representations are geometrically expressive but engineering-inert. They carry no feature history, no constraint graph, no dimensional intent. A mesh cannot be toleranced. A mesh cannot propagate a design change. A mesh cannot be submitted to a manufacturer without full reconstruction from scratch. Parametric CAD, by contrast, encodes design intent as a structured sequence of operations — extrusions, revolves, fillets, boolean operations each governed by explicit dimensional constraints and parent-child dependency relationships. The parametric model is not merely a shape; it is a design process, replayable, modifiable, and transferable across manufacturing contexts. The meaningful technical question for AI CAD in 2026 is therefore not " can AI generate a 3D shape? " that has been demonstrable since 2019. The question is: can AI generate a valid parametric feature tree from natural language input, with embedded manufacturing constraints, that survives downstream engineering use? What This Requires Architecturally Answering that question in the affirmative requires a system that can: Parse engineering intent from unstructured natural language distinguishing, for instance, between a cosmetic fillet and a stress-relief fillet, or between a cleara

Hello everyone! It has been a busy week, but I've made some exciting progress on my machine learning journey. Here is what I've been up to: Kaggle Orbit Wars & AWS I completed the baseline implementation for the Kaggle Orbit Wars competition and initially hit a score of around 1030. My score has dipped slightly over the past few days, so I am currently brainstorming ways to improve it. This week also marked my very first time using AWS! I used it to extract data for reinforcement learning. Transparency check: I spent exactly $7.58 USD on AWS resources during the process. Paper Reading & RL Insights I spent a lot of time reading research papers this week. AlphaZero: I was initially excited about using the self-play mechanism from AlphaZero. However, because this specific game has rock-paper-scissors dynamics, standard self-play might not work effectively. AlphaStar: This led me to the AlphaStar paper, which uses self-play combined with League Training . The engineering behind AlphaStar is incredible. Two specific concepts really stood out to me: Pointer Networks and V-trace off-policy correction . I was also impressed by their use of an LSTM core to handle long-term memory. Next Steps Moving forward, I plan to leverage Kaggle, AWS, and GCP credits to train different components of my model. I am giving myself total freedom to experiment, imagine, and test unconventional solutions. Random life update to close out the week: I used to have long hair because I was insecure about my forehead, but I finally decided to shave it all off at home by myself. It honestly feels really weird right now, but it's a fresh start!

submitted by /u/esporx [link] [留言]

On August 19, Startup Battlefield is returning to Sydney in partnership with Stripe, one of the world's most iconic technology companies. We're taking over Stripe Tour Sydney for a night that the Australian startup ecosystem won't forget.

These to seem to be confused and mixed up often. How do you pick those apart? submitted by /u/phamsung [link] [留言]

Block prompt inject & cut token costs for AI browser agents Discussion | Link

Paying users will have tools for reaching either wider or more-specific audiences.

Estonian government benchmark shows how dozens of models combat Russia's "strategic narratives."

Meta was previously reported to be exploring facial recognition for its smart glasses.

Built a browser-based FFmpeg editor that runs entirely client-side via WebAssembly. Your files never leave your device -- all processing happens in a Web Worker. Works offline as an installable PWA after first load.

OpenAI has significantly improved the chatbot's "dreaming" architecture.