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The article explores the complex relationship between history and flattery, discussing the challenges historians face in maintaining objectivity and resisting the temptation to distort the past to appease powerful figures or popular sentiment. It emphasizes the importance of historical integrity and the need for historians to remain impartial and committed to the truth, even when it may not align with prevailing narratives.

PR Bro is an open-source command-line tool that helps developers manage and review pull requests more efficiently. It provides features like pull request status tracking, comment filtering, and a streamlined workflow to improve the pull request review process.

The article discusses the potential of using machine learning to improve the prediction of mortality rates for patients with heart failure. It explores the development and evaluation of a machine learning model that aims to enhance the accuracy of mortality prediction compared to traditional methods.

Ataraxy Labs introduces SEM, a novel machine learning framework that combines symbolic and subsymbolic approaches to tackle complex real-world problems. SEM aims to leverage the strengths of both symbolic and subsymbolic techniques to achieve more robust and interpretable models.

The article explores the development of Total Recall, a groundbreaking science fiction film directed by Paul Verhoeven and starring Arnold Schwarzenegger. It delves into the creative process, technical challenges, and the lasting impact of this iconic movie on the genre.

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.

Kilo Pass is a password management solution that enables secure storage and sharing of passwords across devices and teams. It offers features such as password generation, autofill, and two-factor authentication to enhance security and convenience for users.

The article provides a step-by-step guide on how to create a Hytale server, including setting up a server, installing the necessary software, and configuring the server settings to create a custom gaming experience for players.

AWS Revenue: ~$35B (up 24% yoy, fastest growth in 13 quarters)

AWS Operating Income: ~$12B (up 17% yoy)

"AI, chips, robotics, and low earth orbit satellites: ..we expect to invest about $200 billion in capital expenditures across Amazon in 2026.."

This is eating into Free Cash Flow (FCF) which shows a downtrend over the last 5 quarters to ~$11B. In contrast MSFT free cashflow also down to ~$5B. (FCF = Operating cash flow - capex)

Custom chips: Trainium and Graviton now have a combined annual revenue run rate of over $10 billion and growing at a triple digit percentage year-over-year.

Lots of agent-related new releases in this quarter: Nova Act (for building AI agents for UI workflows); Amazon Bedrock AgentCore (infrastructure building blocks to build agents); "frontier agents" Kira, AWS Security Agent, AWS DevOps Agent; Agentic AI capabilities for Amazon Connect (call center platform)

For years, SEO has meant optimizing for Google’s crawler.

But increasingly, discovery seems to be happening somewhere else: ChatGPT Claude Perplexity AI-powered search and assistants

These systems don’t “rank pages” the same way search engines do. They select sources, summarize them, and recommend them directly.

What surprised me while digging into this: - AI models actively fetch pages from sites (sometimes user-triggered, sometimes system-driven) - Certain pages get repeatedly accessed by AI while others never do - Mentions and recommendations seem to correlate more with contextual coverage and source authority than traditional keyword targeting

The problem is that this entire layer is invisible to most builders.

Analytics tools show humans. SEO tools show Google. But AI traffic, fetches, and mentions are basically a black box.

I started thinking about this shift as: GEO (Generative Engine Optimization) or AEO (Answer Engine Optimization)

Not as buzzwords, but as a real change in who we’re optimizing for.

To understand it better, I ended up building a small internal tool (LLMSignal) just to observe: - when AI systems touch a site - which pages they read - when a brand shows up in AI responses

The biggest takeaway so far: If AI is becoming a front door to the internet, most sites have no idea whether that door even opens for them.

Curious how others here are thinking about: - optimizing for AI vs search - whether SEO will adapt or be replaced - how much visibility builders should even want into AI systems

Not trying to sell anything — genuinely interested in how people here see this evolving.

The article explores the challenges facing the luxury industry as it navigates the COVID-19 pandemic, including shifts in consumer behavior and the need for companies to adapt their strategies to remain relevant and competitive.

The article discusses the recent launch of the James Webb Space Telescope, the most powerful space observatory ever built, which is expected to provide unprecedented insights into the early universe and the formation of galaxies.

This article discusses the revelation that actor Chris Hemsworth holds an L9 level position at Amazon, which is typically reserved for senior executives, raising questions about the company's compensation structure and the role of celebrity in the tech industry.

The article discusses a study that found listening to 'pink noise' can reduce rapid eye movement (REM) sleep, potentially harming overall sleep quality. Researchers suggest caution when using pink noise, as it may have unintended consequences on sleep patterns.

GlyphBox is a web-based tool that allows users to create and customize unique typographic designs using a library of glyphs. The application provides a range of features for manipulating text, adjusting layout, and exporting the final design as an image or vector file.

Moltbook is a new social media platform designed specifically for AI agents, allowing them to collaborate, share knowledge, and engage in discussions. The platform aims to foster a community of AI developers and researchers to accelerate the progress of artificial intelligence.

Autropic is a startup that creates sustainable, biodegradable products to reduce plastic waste. The company offers a range of personal care and household items made from natural materials like bamboo, sugarcane, and seaweed.

The article discusses the recent layoffs at The Washington Post and the controversy surrounding Jeff Bezos, the owner of the newspaper, who was seen vacationing on his superyacht while the layoffs were announced. The article examines the implications of these events and the broader challenges facing the media industry.

The article discusses a new mathematical proof that challenges the fundamental law of probability, suggesting that the outcomes of dice rolls may not be as random as commonly believed. The findings could have implications for various fields, including gambling, cryptography, and the foundations of quantum mechanics.

Curious to know your experience running local LLM's with a well spec'ed out M3 Ultra or M4 Pro Mac Studio. I don't see a lot of discussion on the Mac Studio for Local LLMs but it seems like you could put big models in memory with the shared VRAM. I assume that the token generation would be slow, but you might get higher quality results because you can put larger models in memory.

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

Cryptographic toolkit in a single HTML file. Sign, encrypt, verify - all offline, no dependencies.