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Introducing Coregit

Coregit reimplements Git's object model inTypeScriptand runs onCloudflare Workersas a serverless edge Git API. Its commit endpoint accepts up to 1,000 file changes per request and replaces 105+ GitHub calls with one. Yes - one. It acknowledges writes inDurable Objects(~2ms), then flushes objects toR.. read more  

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A GitHub agentic workflow

The developer automated parsing of unstructured release notes withGitHub agentic workflows. The pipeline compilesMarkdowntoYAML, then runs an agent. The setup requires afine-grained Copilot token. It enforces a hardenedsandboxpolicy and forbids Marketplace actions. CI runs a compile-then-compare che.. read more  

A GitHub agentic workflow
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Introducing Ternary Bonsai: Top Intelligence at 1.58 Bits

PrismML unveilsTernary Bonsai: a family of1.58-bitLMs in1.7B,4B, and8Bsizes. Models use ternary weights {-1,0,+1} with group-wise quantization. Weights are ternary (-1,0,+1). Each group of128weights shares anFP16scale. That cuts memory by ~9x versus 16-bit and boosts benchmark scores. The8Bhits 75.5.. read more  

Introducing Ternary Bonsai: Top Intelligence at 1.58 Bits
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How LLMs Work — A Visual Deep Dive

A complete walkthrough of how large language models like ChatGPT are built, from raw internet text to a conversational assistant... read more  

How LLMs Work — A Visual Deep Dive
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The PR you would have opened yourself

ASkillports models fromtransformerstomlx-lm. It bootstraps an env, discovers variants, downloads checkpoints, writes MLX implementations, and runs layered tests. It produces disclosed PRs with per-layer diffs, dtype checks, generation examples, numerical comparisons, and a reproducible, non-agentict.. read more  

The PR you would have opened yourself
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What is AWS Graviton? The custom chip powering applications for 90,000 customers

Amazon'sGravitonfamily peaks at a 192-core chip. It delivers up to25%better performance thanGraviton4and keeps energy efficiency intact. AWS says98%of its top 1,000 EC2 customers runGraviton. More than half of new EC2 capacity runs on these chips... read more  

What is AWS Graviton? The custom chip powering applications for 90,000 customers
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Betterleaks: The Gitleaks Successor Built for Faster Secrets Scanning

BetterleakssupplantsGitleaksas a drop-in CLI. Scans run faster. It's written inPure Go- no CGO - and performs parallel git scans. It replaces entropy heuristics with token-efficient detection viaBPE. It addsCELrule validation. Its roadmap includes LLM assist and auto-revocation... read more  

Betterleaks: The Gitleaks Successor Built for Faster Secrets Scanning
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Why We Chose the Harder Path: Hardened Images, One Year Later

Docker Hardened Images surpassed500k daily pullsand now hosts2,000+ hardened images, all built in aSLSA Build Level 3pipeline. It compiles tens of thousands ofDebianandAlpinepackages from source. It runs 1M+ builds. It ships17 signed attestationsper image. It auto-rebuilds customized images under SL.. read more  

Why We Chose the Harder Path: Hardened Images, One Year Later
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pgit: I Imported the Linux Kernel into PostgreSQL

pgitingested 20 years of the Linux kernel: 1.43M commits, 24.4M file versions. The dataset lives inPostgreSQLwithpg-xpatch- 2.7GB on disk. A 2-hour import on a 24-core EPYC built a queryableSQLDB. Most delta-decompressed queries return in <10s. No preprocessing required... read more  

pgit: I Imported the Linux Kernel into PostgreSQL
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Post-Quantum Cryptography Migration at Meta: Framework, Lessons, and Takeaways

Quantum computers could decrypt data stored today in anticipation of future decryption, posing security risks despite the estimated decade-long timeline. Industry-wide PQC standards are being published by NIST to defend against such threats, including algorithms like ML-KEM and ML-DSA. The industry .. read more  

Post-Quantum Cryptography Migration at Meta: Framework, Lessons, and Takeaways
GPT (Generative Pre-trained Transformer) is a deep learning model developed by OpenAI that has been pre-trained on massive amounts of text data using unsupervised learning techniques. GPT is designed to generate human-like text in response to prompts, and it is capable of performing a variety of natural language processing tasks, including language translation, summarization, and question-answering. The model is based on the transformer architecture, which allows it to handle long-range dependencies and generate coherent, fluent text. GPT has been used in a wide range of applications, including chatbots, language translation, and content generation.

GPT is a family of language models that have been trained on large amounts of text data using a technique called unsupervised learning. The model is pre-trained on a diverse range of text sources, including books, articles, and web pages, which allows it to capture a broad range of language patterns and styles. Once trained, GPT can be fine-tuned on specific tasks, such as language translation or question-answering, by providing it with task-specific data.

One of the key features of GPT is its ability to generate coherent and fluent text that is indistinguishable from human-generated text. This is achieved by training the model to predict the next word in a sentence given the previous words. GPT also uses a technique called attention, which allows it to focus on relevant parts of the input text when generating a response.

GPT has become increasingly popular in recent years, particularly in the field of natural language processing. The model has been used in a wide range of applications, including chatbots, content generation, and language translation. GPT has also been used to create AI-generated stories, poetry, and even music.