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Anthropic announces the release of Claude, a large language model with improved capabilities in language understanding, generation, and reasoning. Claude Opus 4.6 introduces several enhancements, including better long-form coherence, more consistent persona, and improved factual accuracy.

This article explores the increasing adoption of PostgreSQL as a versatile and powerful database solution, highlighting its ability to handle a wide range of modern data management needs, including support for JSON, full-text search, and spatial data.

This article covers the development of a C compiler by Anthropic engineers. It discusses the challenges and design decisions involved in building a robust and efficient compiler, including parsing, type checking, code generation, and optimization.

The article discusses a Chrome extension that can be used to track users' browsing behavior and LinkedIn activity, raising privacy concerns. It explains how the extension can be used to fingerprint and monitor users without their knowledge or consent.

This article discusses the concept of agent teams in the Claude platform, which allows developers to create and manage groups of AI agents to handle complex tasks collaboratively. It covers the key features and benefits of agent teams, such as improved scalability, task distribution, and coordination among multiple agents.

The article discusses a new report on the state of the tech industry, highlighting key findings such as industry growth, emerging technologies, and workforce trends. It provides a comprehensive overview of the current landscape and future outlook for the tech sector.

Opus 4.6 is a new finance platform that aims to revolutionize the way individuals manage their money. It offers a range of features, including AI-powered financial planning, automated portfolio management, and personalized investment recommendations.

The article discusses the launch of Claude Opus 4.6, a new version of the Claude AI assistant, and a limited-time offer for extra usage promo. It provides details on the new features and improvements in Opus 4.6, as well as information on the promotional offer and how users can take advantage of it.

The article discusses the ongoing power outages in Cuba and the impact of U.S. sanctions on the country's ability to import oil and maintain its electrical grid. It also explores the role of the Trump administration's policies in exacerbating the situation and the challenges Cuba faces in addressing the crisis.

The article discusses a GitHub repository called 'skills' that provides a set of common skills and capabilities for various programming languages and frameworks. The repository aims to serve as a resource for developers to improve their skills and knowledge across different technologies.

The article discusses the benefits of using expect tests in software development, highlighting their ability to catch unexpected behavior, promote cleaner code, and facilitate better testing practices.

The article discusses the merits and challenges of the idea that we live in a computer simulation, exploring philosophical and scientific perspectives on the topic. It examines the implications of this hypothesis and the ongoing debate around its feasibility and testability.

The article argues that C is not a programming language, but rather a collection of conventions, idioms, and a set of language primitives. It suggests that C is better understood as a tool kit or framework for building programming languages, rather than a standalone language.

The article discusses the growing popularity of piracy streaming boxes, which allow users to access copyrighted content for free, and the legal challenges faced by content creators and distributors in addressing this issue.

The article discusses the impact of Canada's decision to allow the sale of Chinese-made vehicles, which has caused a sense of panic among U.S. car dealers who fear increased competition from lower-priced Chinese models entering the North American market.

Hi HN, I am shubham a 3d artist who learned coding in college as an I.T. graduate know logics but not an expert as i just wanna try my hands on to ai

So i built Resilient Workflow Sentinel this is offline ai agent which classify urgency (Low,Medium and HIgh) and dispatches to the candidates based on availability Well i want an offline system like a person can trust with its sensitive data to stay completely locally

Did use ai to code for speeding and cutting labor.

Its works on RTX 3080 system (this is an basic affordable setup not heavy ai machinery) which i want it to make it reliable without heavy upgrade This is full system doesn't require ollama(I am not against it)

I see in companies tickets are raised on jira and slack. Currently people or manager (self) have to sort those things either manually read one by one or send them to the cloud. But the issue is you can't send everything like there is a lot of sensitive data out there which they do not trust and makes it harder and manual sorting through thousands is likely a nightmare.

But then just imagine u get all the task classified like its urgency and distribution u can selectively see which task is urgent and needs immediate attention and last of all information doesn't leave your building totally secure Also Api sending is not the only issue u are paying per token cost for task for each may be monthly 100$ to 1000$ which can like save hassle for startup a lot or companies as well

There was several biases like positional bias also json out put bias also have issues in attention At start i tried just prompting things like Chain of thoughts,RISE(evaluate negative first), given negative examples,Positive examples, somewhere it was struggling with commonsense issue so examples for that (Later changed the approach)

Well prompting did give the output and worked well but took too much time to process for single task like 70 to 90secs for a task

Then i tried batching and the biases got worst like it got stronger it always use to like favour alice also more prompts are like ignored and more

For json output i used constrain so model can only generate json and if fails there is a as well parser i used when i implemented prompting only

This reduce time from 90sec to nearly 15 to 30secs per task I used steering vector to correct the attention i seen issues happening

Stack: Language: Python 3.10 Model: qwen2.5-7b-instruct Libraries: Pytorch, Hugging Face Transformers (No Langchain, No Ollama) API: Fast API UI: NiceGUI Hardware: Ryzen 5, 16Gb ram RTX 3080

Implementation:

Quantization: Load model in nf4 quantization so models like 7b can fit on vram of 10gb which is on rtx 3080 also my hardware

Steering Vectors: Standard prompting wasn't enough. I need to block or direct certain things on a certain layer of llm to make it reliable.

Json Constraints: Used constraint to make model strictly give json and also stop from over explanation this happens at logits level where token are blocked which are not required etc

github : https://github.com/resilientworkflowsentinel/resilient-workf...

Youtube: https://youtu.be/tky3eURLzWo

The U.S. Department of Homeland Security is seeking to compel technology companies to help them access encrypted communications, raising concerns about user privacy and the balance between national security and civil liberties.

The article discusses the expansion of facial recognition technology by U.S. immigration agencies, including the use of mobile devices for identity verification and increased data sharing across government agencies. It highlights concerns about privacy and civil liberties raised by the increasing use of these technologies.

INSTALL EXTENSION vsql-complex; CREATE TABLE t (val COMPLEX);

Look, MySQL is awesome [flamewar incoming?]. But the ecosystem has stagnated. Why?

No permissionless innovation. Postgres has flourished because people can change the core of the database (look at pgvector and pg_textsearch), without having to get their changes accepted upstream.

(This, btw, is what powered GitHub's early success: you can fork a repo and make changes without needing the owners' approval)

VillageSQL is a tracking fork of MySQL (open source, ofc) that adds an extension framework: * Drop-in replacement

* Add custom data types and functions (with indexes coming soon)

* we wrote example extensions (vsql-ai, -uuid, crypto, etc.)

* you have a better idea for an extension

* my CEO submitted a Show HN post but linked to the announcement blog; help me show him hackers want code first

* I'm particularly proud of the friendly C++ API to add custom functions (in func_builder.h)

That link again is https://github.com/villagesql/villagesql-server

(Oh, and I get to work with the former TL of Google's BigTable and Colossus, so we care about doing databases Right)

I think agents should work like real teams, with independent, distinct roles, async communication, and the ability to onboard new teammates or tools without restructuring the whole org. I built backend systems at Yahoo and TikTok so event-driven agents felt obvious. But no agent SDKs were using this pattern, so I made Calfkit.

Calfkit breaks down agents into independent services (LLM inference, tools, and routing) that communicate asynchronously through Kafka. Agents, tool services, and downstream consumers can be deployed, added-to, removed, and scaled independently.

Check it out if this interests you! I’m curious to see what y’all think.