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This article explores the fundamentals of logistic regression, explaining the sigmoid function, log loss, and how these concepts are used in binary classification problems. It provides a comprehensive understanding of the underlying mathematics and practical applications of this machine learning technique.

The article discusses the release of ChromeOS Flex, a new operating system from Google that allows users to convert older Windows or macOS laptops into Chromebooks. It highlights the key features, benefits, and target audience of ChromeOS Flex, providing an overview of this new ChromeOS variant.

This article provides a step-by-step guide for building a simple chat application using C# and the .NET framework. It covers topics such as implementing a client-server architecture, managing client connections, and handling real-time messaging between clients.

The article discusses the KimWolf botnet, a new malware threat targeting home and small business networks. The botnet is designed to infiltrate local networks, scan for vulnerable devices, and potentially use them to launch further attacks or spread the malware.

Panda diplomacy refers to the practice of gifting or loaning giant pandas by China to other countries as a soft power tool to build relationships and showcase Chinese culture. This article discusses the history, motivations, and impact of this diplomatic strategy.

The article discusses the recent investment by Blackstone CEO Stephen Schwarzman and OpenAI co-founder Greg Brockman in a new AI startup called Anthropic, which aims to develop safe and ethical AI systems. The investment is seen as a significant boost for the AI industry, as Schwarzman and Brockman are high-profile figures with extensive experience in the field.

The article discusses the Ruby Wrapped, a new Ruby extension that provides a clean and powerful wrapper around the standard Ruby object model. It highlights the extension's ability to add new behaviors to existing classes without modifying the original code.

Tech startups are providing free nicotine pouches to employees in an effort to boost productivity and focus, citing potential benefits such as improved cognitive function and reduced stress. However, experts caution that these products carry health risks and may promote addiction.

Has anyone transitioned out of software engineering and regretted it?

I'm a new graduate software engineer and am not sure I want to do this career long-term. I currently live in one of (NYC/SF/Seattle) and don't really like it. People seem very career focused which I do not think describes me very well. I have visited the other two cities and they seem much the same so I don't feel like looking for a new job (and given the market probably cannot) as people will likely still be as career focused.

I have tried to look for remote roles but those largely seem non-existent given my current stage. I've thought that I could try and stay at my current role till I qualify as a Senior Engineer as most remote jobs seem to want that, but I think I'll be pretty disappointed if I get there and can't get a remote job. I've generally thought before that the goal should be to enjoy the journey and not the destination but I'm not really enjoying the journey.

Lastly, I have considered startups as the work seems more interesting but I am not very interested in working 6 to 7 days a week. I'd be fine with long-hours Monday through Friday but even weekends (regularly) starts to feel like I am property of the company.

I am thinking of going back to school for a different profession since I do have decent savings at least.

Sorry for the wall of text.

The article discusses a new NASA mission called Einstein Probe, which is designed to monitor nearby stars for X-ray flares that could provide insights into the nature of dark matter and dark energy. The probe will utilize advanced X-ray telescopes to study these cosmic events and their potential impact on the search for dark matter.

The article discusses the possibility of OpenAI, the artificial intelligence company, making a move on Pinterest, the social media platform. It explores the potential strategic and business implications of such a move, considering the capabilities of OpenAI's language models and their possible applications in the context of Pinterest's visual search and content curation functionalities.

The .NET Core team is considering adding a feature to provide the ability to control the version of .NET Core used by an application, allowing for more flexibility in managing .NET Core dependencies across different projects.

The article reports that banks are increasingly tapping into the New York Federal Reserve's standing repo facility, a sign that the facility is providing much-needed liquidity to the financial system during a period of market volatility.

The article discusses the concept of Fast Inverse Square Root, a highly efficient algorithm for calculating the reciprocal square root of a number. It provides an in-depth analysis of the algorithm's implementation and its underlying mathematical principles.

The article discusses the concept of liveness in the context of Temporal Logic of Actions (TLA), a formal specification language. It explains how TLA can be used to verify the liveness properties of concurrent and distributed systems, ensuring they make progress and eventually reach a desired state.

The article reports that Apple has reportedly axed production of its Vision Pro headset, despite plans for a newer M5 model. Additionally, marketing for the Vision Pro has dropped by more than 95% in recent weeks.

The article discusses the differences between the CSS `:first-child` and `:nth-child()` selectors, explaining how they select elements based on their position within their parent element. It also covers the `:next-sibling` selector and how it can be used to target elements that come after a specific element.

Fanimal, a ticketing startup, has filed an antitrust lawsuit against Ticketmaster, alleging that the company used anticompetitive practices to force Fanimal out of the market and maintain its dominant position in the ticketing industry.

Hi Hacker News,

This is definitely out of my comfort zone. I've never programmed DSP before. But I was able to use Claude code and have it help me build this using CMajor.

I just wanted to show you guys because I'm super proud of it. It's a 100% faithful recreation based off of the schematics, patents, and ROMs that were found online.

So please watch the video and tell me what you think

https://youtu.be/auOlZXI1VxA

The reason why I think this is relevant is because I've been a programmer for 25 years and AI scares the shit out of me.

I'm not a programmer anymore. I'm something else now. I don't know what it is but it's multi-disciplinary, and it doesn't involve writing code myself--for better or worse!

Thanks!

The article discusses Uxn, a minimalistic virtual machine designed for creative coding and retro-style computing. It provides an overview of Uxn's architecture, including its stack-based instruction set and memory model, as well as its potential applications in various domains such as games, tools, and embedded systems.

I built this service + client because there's not a reasonable way to share Claude Code + Codex CLI transcripts.

All data is e2e encrypted before it leaves your device, the share url contains the decryption key.

Self-host on Cloudflare workers if you prefer

I made these two extremely simple shortcuts a while back to quickly translate and read aloud into earphones info presented in a French art museum.

It allowed my family and me to look at the art while listening to information about it just like an audio guide would. This was the fastest implementation of something like this (two shortcuts on an iPhone Home Screen) I could think of, and you can even map the Action Button to it.

I could also see this being useful from an accessibility perspective, as it is great for people who only need text read out loud to them sometimes, while still being very easily accessible with a single button press.

I have been building a local AI memory layer for a while, and the same problem shows up every time you try to make an assistant feel stateful.

The agent is impressive in the moment, then it forgets. Or it remembers the wrong thing and hardens it into a permanent belief. A one off comment becomes identity. A stray sentence becomes a durable trait. That is not a model quality issue. It is a state management issue.

Most people talk about memory as “more context.” Bigger windows, more retrieval, more prompt stuffing. That is fine for chatbots. Agents are different. Agents plan, execute, update beliefs, and come back tomorrow. Once you cross that line, memory stops being a feature and becomes infrastructure.

The mental model I keep coming back to is an operating system.

1.What gets stored 2.What gets compressed 3.What gets promoted from “maybe” to “true” 4.What decays 5.What gets deleted 6.What should never become durable memory in the first place

If you look at what most memory stacks do today, the pipeline is basically the same everywhere.

Capture the interaction. Summarize or extract. Embed. Store vectors and metadata. Retrieve. Inject into the prompt. Write back new memories.

That loop is not inherently wrong. The bigger issue is where the loop runs. In a lot of real deployments, the most sensitive parts happen outside the user’s environment. Raw interactions get shipped out early, before you have minimized or redacted anything, and before you have decided what should become durable.

When memory goes cloud first, the security model gets messy in a very specific way. Memory tends to multiply across systems. One interaction becomes raw snippets, summaries, embeddings, metadata, and retrieval traces. Even if each artifact feels harmless alone, the combined system can reconstruct a person’s history with uncomfortable fidelity.

Then there is the trust boundary problem. If retrieved memories are treated as trusted context, retrieval becomes a place where prompt injection and poisoning can persist. A bad instruction that gets written into memory does not just affect one response. It can keep resurfacing later as “truth” unless you have governance that looks like validation, quarantine, deletion, and audit.

Centralized memory also becomes a high value target. It is not just user data, it is organized intent and preference, indexed for search. That is exactly what attackers want.

And even if you ignore security, cloud introduces latency coupling. If your agent reads and writes memory constantly, you are paying a network tax on the most frequent operations in the system.

This is why I think the edge is not a constraint. It is the point. If memory is identity, identity should not default to leaving the device.

There is also a hardware angle that matters as agents become more persistent. CXL is interesting here because it enables memory pooling. Instead of each machine being an island, memory can be disaggregated and allocated as a shared resource. That does not magically create infinite context, but it does push the stack toward treating agent state as a real managed substrate, not just tokens.

My bet for 2026 is simple. The winning agent architectures will separate cognition from maintenance. Use smaller local models for the repetitive memory work like summarization, extraction, tagging, redundancy checks, and promotion decisions. Reserve larger models for the rare moments that need heavy reasoning. Keep durable state on disk so it survives restarts, can be inspected, and can actually be deleted.

Curious what others are seeing. For people building agents, what is the biggest blocker to running memory locally today: model quality, tooling, deployment, evaluation, or something else?

The article explores the author's search for information about their grandmother Alice, who disappeared without a trace in 1956. It delves into the family's attempts to uncover the mystery surrounding her disappearance and the impact it has had on subsequent generations.