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@kaptain shared a link, 2 weeks, 1 day ago
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The AI-driven shift in vulnerability discovery: What maintainers and bug finders need to know

AI modelslet non-experts craft real and fake vulnerabilities at scale. They spit out low-quality noise and the occasional high-value report. Reports floodOSS maintainers. Triage, patching, release cadences, and downstreamupgrade/compliancepipelines buckle under the load. Guidance recommends publishi.. read more  

The AI-driven shift in vulnerability discovery: What maintainers and bug finders need to know
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@kala shared a link, 2 weeks, 1 day ago
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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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@kala shared a link, 2 weeks, 1 day ago
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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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@kala shared a link, 2 weeks, 1 day ago
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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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@kala shared a link, 2 weeks, 1 day ago
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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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@kala shared a link, 2 weeks, 1 day ago
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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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@devopslinks shared a link, 2 weeks, 1 day ago
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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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@devopslinks shared a link, 2 weeks, 1 day ago
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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
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@devopslinks shared a link, 2 weeks, 1 day ago
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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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@devopslinks shared a link, 2 weeks, 1 day ago
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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
BigQuery is a cloud-native, serverless analytics platform designed to store, query, and analyze massive volumes of structured and semi-structured data using standard SQL. It separates storage from compute, automatically scales resources, and eliminates the need for infrastructure management, indexing, or capacity planning.

BigQuery is optimized for analytical workloads such as business intelligence, log analysis, data science, and machine learning. It supports real-time data ingestion via streaming, batch loading from cloud storage, and federated queries across external data sources like Cloud Storage, Bigtable, and Google Drive.

Query execution is distributed and highly parallel, enabling interactive performance even on petabyte-scale datasets. The platform integrates deeply with the Google Cloud ecosystem, including Looker for BI, Vertex AI for ML workflows, Dataflow for streaming pipelines, and BigQuery ML, which allows users to train and run machine learning models directly using SQL.

Built-in security features include fine-grained IAM controls, column- and row-level security, encryption by default, and audit logging. BigQuery follows a consumption-based pricing model, charging for storage and queries (on-demand or reserved capacity).