Anthropic buys biotech startup Coefficient Bio in $400M deal: Reports - TechCrunch
Anthropic buys biotech startup Coefficient Bio in $400M deal: Reports TechCrunch
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RTX 5090 gemma4-26b TG performance report
Nothing exhaustive... but I thought I'd report what I've seen from early testing. I'm running a modified version of vLLM that has NVFP4 support for gemma4-26b. Weights come in around 15.76 GiB and the remainder is KV cache. I'm running full context as well. For a "story telling" prompt and raw output with no thinking, I'm seeing about 150 t/s on TG. TTFT in streaming mode is about 80ms. Quality is good! submitted by /u/Nice_Cellist_7595 [link] [comments]
![[P] SpeakFlow - AI Dialogue Practice Coach with GLM 5.1](https://d2xsxph8kpxj0f.cloudfront.net/310419663032563854/konzwo8nGf8Z4uZsMefwMr/default-img-matrix-rain-CvjLrWJiXfamUnvj5xT9J9.webp)
[P] SpeakFlow - AI Dialogue Practice Coach with GLM 5.1
Built SpeakFlow for the Z.AI Builder Series hackathon. AI dialogue practice coach that evaluates your spoken responses in real-time. Two modes: Practice (AI evaluates accuracy/grammar/fluency) and Presentation (lines hidden, hints available, instant scoring). Supports 11 languages, audio recording, detailed session reports. Built with: HTML5, Web Speech API, GLM 5.1, Vercel Live: https://speakflow-omega.vercel.app Code: https://github.com/ArjunGanjinalli/speakflow #buildwithGLM submitted by /u/Future-Ad-1493 [link] [comments]
The $200 Billion Wait: How Outdated Banking Rails Are Strangling the Global Workforce
The Scene It’s 4:45 PM in Singapore on a Friday. The CFO of a Series B AI startup has just clicked “approve” on the month’s payroll. Her team of 47 is scattered across 12 countries: core engineers in Bangalore, prompt specialists in Warsaw, a compliance lead in Mexico City, and a newly hired head of growth in Lagos. The company’s runway is tight, and morale is fragile. She knows, with a sinking feeling, that the $187,000 she just released won’t land in her team’s accounts for 3 to 5 business days. For the engineer in Nigeria, where weekend banking is a fiction, it could be next Wednesday. She’s just authorized the payments, but she’s lost all control. The money is now in a labyrinth of correspondent banks, each taking a cut and adding a delay, with zero transparency. One employee will inev
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hexagon: slight optimization for argosrt output init ( #21463 ) macOS/iOS: macOS Apple Silicon (arm64) macOS Intel (x64) iOS XCFramework Linux: Ubuntu x64 (CPU) Ubuntu arm64 (CPU) Ubuntu s390x (CPU) Ubuntu x64 (Vulkan) Ubuntu arm64 (Vulkan) Ubuntu x64 (ROCm 7.2) Ubuntu x64 (OpenVINO) Windows: Windows x64 (CPU) Windows arm64 (CPU) Windows x64 (CUDA 12) - CUDA 12.4 DLLs Windows x64 (CUDA 13) - CUDA 13.1 DLLs Windows x64 (Vulkan) Windows x64 (SYCL) Windows x64 (HIP) openEuler: openEuler x86 (310p) openEuler x86 (910b, ACL Graph) openEuler aarch64 (310p) openEuler aarch64 (910b, ACL Graph)
Stop Prompting; Use the Design-Log Method to Build Predictable Tools
The article by Yoav Abrahami introduces the Design-Log Methodology, a structured approach to using AI in software development that combats the "context wall" — where AI models lose track of project history and make inconsistent decisions as codebases grow. The core idea is to maintain a version-controlled ./design-log/ folder in a Git repository, filled with markdown documents that capture design decisions, discussions, and implementation plans at the time they were made. This log acts as a shared brain between the developer and the AI, enabling the AI to act as a collaborative architect rather than just a code generator. By enforcing rules like read before you write, design before implementation, and immutable history, the methodology ensures consistency, reduces errors, and makes AI-assi
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