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The Z Fold 8 Ultra's improved performance, faster charging and a new ultra-wide camera are nice, but what we really want is the regular model's wider screens.

The new base version of the Z Fold 8 has reworked aspect ratios for both its interior and exterior displays.

We've had Samsung's latest Ultra smartwatch for about a week, and here are our first impressions.

It's a year of minor upgrades for the Z Flip series.

Thinner watches, wider foldables, higher prices.

People spend hours watching videos on their smartphones. The wider, shorter, and lighter Galaxy Z Fold8 caters to exactly that.

It's a year of refinement for the Galaxy Watch. With the new Galaxy Watch 9 and Galaxy Watch Ultra 2, which are being announced today, Samsung is more or less taking what people like about its watches and tweaking those elements to be better. The company has been doing this for the past few generations […]

Samsung’s folding phone family has a new middle child with a fresh form factor, but which of its eighth-gen foldables is right for you?

WhatsApp on Apple CarPlay and Android Auto will let users hear and reply to messages, make calls, view their call history, and access favorite contacts hands-free and right from their car's screen.

Passionfroot, a German startup building a marketplace connecting B2B creators with brands, has raised $15M in a Series A round led by Insight Partners.

OpenAI announces Project Camellia in Effingham County, Georgia, with commitments to responsible energy, community investment, jobs, and access to Codex.

I spent months building an AI agent platform. I launched it on Product Hunt yesterday. Zero signups. The comments were friendly. Three of the four asked for the same thing — not features, not integrations, not a lower price. They wanted to see what the agents did and what they cost . One put it better than my own landing page ever did: they liked that it wasn't "a black box." So I went to make the cost dashboard better. Instead I found out my product had been lying to me for months, and the lies had a pattern. Here's everything, with the code. 1. Every run cost $0.00 The Cost Analytics page reported $0.02 in total across ~100 executions . I'd assumed that meant the platform was cheap to run. It meant the data was being destroyed at write time. cost_cents = int ( ( llm_response . prompt_tokens * 0.5 / 1000 ) + ( llm_response . completion_tokens * 1.5 / 1000 ) ) A typical run on my platform is 56 prompt tokens and 45 completion tokens. That's 0.0843 cents . int() makes it 0 . Not some runs. Essentially every run — because almost every LLM call costs less than one cent. The production numbers: 189 agent runs, 2 with a non-zero cost. 99% of my cost data was zeroes, and the two survivors were just big enough to clear a whole cent. The rates in that formula were correct. I checked them against the providers' pricing pages; the arithmetic is right. The bug is entirely int() on a value that is almost never ≥ 1. A Decimal would have been the textbook fix, but Decimal / float raises TypeError and ~80 call sites do arithmetic on this number, so I widened the column to a float and kept the unit (cents). It's a dashboard estimate, not money — Paddle handles money — so float rounding is irrelevant here. 2. Workflow costs were never recorded at all Truncation at least loses precision. This one lost everything. WorkflowExecution.total_cost_cents and total_tokens_used had no write site anywhere in the codebase . Not a broken write — no write. The columns had been NULL since the feat

Before you rename, move, or delete a Python module: who still imports this? Grepping hits strings and comments. Importing the package can run side effects. whoimports walks the tree with the AST and prints every matching import / from … import line. Install pip install git+https://github.com/SybilGambleyyu/whoimports.git Usage whoimports src/auth/session.py whoimports auth.session -f json whoimports pkg.util -f md Notes Zero dependencies, Python 3.10+ File paths and dotted module names Understands src/ layouts text / markdown / JSON output Pairs with gitchurn and redactx for safe refactor context. Source: github.com/SybilGambleyyu/whoimports · MIT

If you've built even a simple AI agent, you've probably noticed that the "agent loop" itself is deceptively simple: the model gets a message, decides whether to call a tool, gets the result back, and repeats until it has an answer. But real-world agents need a lot more than that bare loop to actually work well. What happens when a conversation gets so long it blows past the model's context window? What if a tool call gets interrupted halfway through and leaves your message history in a broken state? What if you want the agent to keep a running todo list of what it's working on, or delegate parts of a task to a specialized sub-agent, or read and write files as part of its job? You could bolt all of this onto your agent manually. Or, if you're using Deep Agents, you get most of it for free through something called middleware . This post walks through what middleware actually is, why Deep Agents ships with a default stack of it, and how each piece behaves, with runnable code for each one so you can see it working instead of just reading about it. So What Is Middleware, Really? If you've done any web development, the term "middleware" probably already rings a bell. It's the same idea here. Middleware is code that sits around the core agent loop and gets a chance to run before or after certain things happen, like before a tool call executes, after the model responds, or right before messages are sent to the model. Instead of writing all of this logic directly inside your agent, you attach separate, independent pieces of middleware that each handle one specific concern. This matters for two reasons: You don't have to build common behaviors from scratch. Things like managing a todo list, summarizing long conversations, or handling file access are problems almost every non-trivial agent runs into. Deep Agents ships default middleware for these so you don't reinvent them every time. You can customize behavior without touching the agent's core logic. Need a custom summarizati

Cut the noise. Keep the stack traces. CI logs are mostly package installs. The failure is a few dozen lines buried under thousands of Downloading… lines. Scrolling for the real stack trace is a tax paid on every red build. logsnip is a zero-dependency Python CLI that extracts those failure regions and collapses the rest. Install pip install git+https://github.com/SybilGambleyyu/logsnip.git # or the whole toolkit: curl -fsSL https://raw.githubusercontent.com/SybilGambleyyu/devkit/main/install.sh | bash One-liners # Last failure from a GitHub Actions run gh run view --log-failed | logsnip --last # Headlines only logsnip ci-full.log --summary # Safe to paste into an AI assistant gh run view --log-failed | logsnip --last | redactx What it matches Built-in patterns cover pytest E lines and AssertionError , npm ERR! , rustc error[E…] , TypeScript error TS… , GitHub Actions ##[error] , make failures, and common exit-code messages. Stack frames after a hit are pulled in automatically. Design No network, no config files, no dependencies — pipe-friendly --check exits 1 when error-like lines appear (CI gate) --json for machines; --summary for humans in a hurry Pairs with redactx before anything leaves your machine Source: github.com/SybilGambleyyu/logsnip · MIT

This week I accidentally created my first vibe coded application. You might ask how one does this accidentally, but it's actually easier than you think. Assumptions Made: The first mistake I made was to assume that Claude remembered my setup and how I like to pair program. I have only had one previous project coded with the help of Claude and GHCP and it's a much more basic application than what I was building this week. I assumed, incorrectly, that Claude would 'remember' how I liked to work and go through the project with me as we make decisions and coding blocks together. The other assumption I made was giving a somewhat blanket approval for Claude to just run with things. I thought at some point Claude would stop, check its understanding during development and then continue but I got excited at the idea of using subagents and once I'd selected that option there was no stopping Claude on its rampage through my code! So What's the Project? I've recently started looking at doing more work with AI rather than just prompting Claude or GHCP to help with the day to day. I wanted to actually call an LLM and sift through information which could then be saved in a DB and recalled at a later point. I set myself a four week plan (with the help of Claude) to help solidify my learning with week one being a basic console application in C# that takes some text, sends to an LLM and then extracts certain information, in this case names of people, which is saved into a Postgres database. Sounds simple right? Where things went right This project started off very interesting. I had a conversation with Claude, same as I would with another developer, about the right LLM to use for this project. We went through the positives, negatives and the potential cost benefits and pitfalls of each option. In the end it was narrowed down to two choices, OpenAI or Gemini. Given that I was experimenting quite a bit and I didn't want to accidentally run up costs, I decided to go with Gemini's free t

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