Exclusive | The Sudden Fall of OpenAI’s Most Hyped Product Since ChatGPT - WSJ
Exclusive | The Sudden Fall of OpenAI’s Most Hyped Product Since ChatGPT WSJ
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Intel's upcoming Wildcat Lake low-budget CPUs leak out again — OEM confirms specs for Core 7 350, Core 5 320, & Core 3 305 in first retail product datasheet
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![[D] ML researcher looking to switch to a product company.](https://d2xsxph8kpxj0f.cloudfront.net/310419663032563854/konzwo8nGf8Z4uZsMefwMr/default-img-wave-pattern-4YWNKzoeu65vYpqRKWMiWf.webp)
[D] ML researcher looking to switch to a product company.
Hey, I am an AI researcher currently working in a deep tech company as a data scientist. Prior to this, I was doing my PhD. My current role involves working ok physics related problems and the project life cycle could be 2-4 years and the change comes in my company very slowly. The problems are quite interesting but because of the slow pace of development, I find myself getting often frustrated. As a byproduct, I don’t think that I am learning as much as I can. Because of these reasons, I want to move to a company where the development cycles are short and you have the flexibility to iterate and test quickly. Ideally a company which directly interacts with customers, like uber. The problem I am facing is that in the interview processes, a lot of these companies require you to have a lot of
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Spent the weekend reading a local agent runtime repo. The TS-only packaging and persistent MCP ports are both very smart.
I like reading local LLM infra repos more than launch posts, and I ended up deep in one this weekend because it supports local providers like Ollama. Two things gave me the “okay, someone actually cared about runtime engineering” reaction. First, the runtime path was moved fully into TypeScript. The API layer, runner orchestration, workspace MCP hosting, and packaging all live there now, and the packaged runtime no longer ships Python source or Python deps. For local/self-hosted stacks that matters more than it sounds: smaller bundle, fewer moving pieces, less cross-language drift. Second, they stopped doing hardcoded MCP port math. Ports are persisted in SQLite with UNIQUE(port) and (workspace_id, app_id) as the key, and the runner merges prepared MCP servers during bootstrap. So local si
I'm shocked (Gemma 4 results)
https://preview.redd.it/xv1p9zp1tdtg1.png?width=1210 format=png auto=webp s=f4cb3b32fd977b3e6d487915de9f985329060342 https://dubesor.de/benchtable 12.Gemma 4 31B (think) in Q4_K_M local - 78.7%. 16.Gemini 3 Flash (think) - 76.5% 19.Claude Sonnet 4 (think) - 74.7% 22.Claude Sonnet 4.5 (no think) - 73.8% 24.Gemma 4 31B (no think) in Q4_K_M local - 73.5%. 29.GPT-5.4 (Think) - 72.8% submitted by /u/Potential-Gold5298 [link] [comments]
Per-Layer Embeddings: A simple explanation of the magic behind the small Gemma 4 models
Many of you seem to have liked my recent post "A simple explanation of the key idea behind TurboQuant" . Now I'm really not much of a blogger and I usually like to invest all my available time into developing Heretic, but there is another really cool new development happening with lots of confusion around it, so I decided to make another quick explainer post. You may have noticed that the brand-new Gemma 4 model family includes two small models: gemma-4-E2B and gemma-4-E4B . Yup, that's an "E", not an "A". Those are neither Mixture-of-Experts (MoE) models, nor dense models in the traditional sense. They are something else entirely, something that enables interesting new performance tradeoffs for inference. What's going on? To understand how these models work, and why they are so cool, let'
Comparing Qwen3.5 vs Gemma4 for Local Agentic Coding
Gemma4 was relased by Google on April 2nd earlier this week and I wanted to see how it performs against Qwen3.5 for local agentic coding. This post is my notes on benchmarking the two model families. I ran two types of tests: Standard llama-bench benchmarks for raw prefill and generation speed Single-shot agentic coding tasks using Open Code to see how these models actually perform on real multi-step coding workflows My pick is Qwen3.5-27B which is still the best model for local agentic coding on an 24GB card (RTX 3090/4090). It is reliable, efficient, produces the cleanest code and fits comfortably on a 4090. Model Gen tok/s Turn(correct) Code Quality VRAM Max Context Gemma4-26B-A4B ~135 3rd Weakest ~21 GB 256K Qwen3.5-35B-A3B ~136 2nd Best structure, wrong API ~23 GB 200K Qwen3.5-27B ~45
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