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Samsung's Galaxy Z Flip 8 is slimmer and there's now more activity on the cover display.

Samsung has officially announced the Galaxy Z Fold 8 Ultra foldable smartphone, the flagship in the expand Z Fold lineup.

Google and Samsung have revealed the Gemini-powered AI tools coming to the new Galaxy foldables and smartwatches.

Samsung has unveiled a base Galaxy Z Fold 8 model that comes in a different shape than previous Fold devices.

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.

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.

Samsung's annual summer event kicked off on Wednesday morning with the company unveiling a new lineup of watches and foldables. Following weeks of rumors and leaks, Samsung announced the Galaxy Z Fold 8 with a completely new design that's shorter and wider than last year's Fold, a new "Ultra" Fold, an updated Flip model, and […]

Samsung's new Galaxy Z Flip 8 feels more like a regular phone than ever. It's thinner, lighter, and has a front screen you can do just about anything on. Whether that's a good or bad thing, I'm not quite sure yet. I spent three hours with Samsung's newest flip phone last week, and it doesn't […]

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 […]

A year after overhauling its Z Fold phone with a radically thinner design, Samsung has changed things up again, this time altering the shape entirely. The Z Fold 8 is wider than any of Samsung's previous foldables, and shorter too, a size and shape somewhere between Samsung's traditional Flip and Fold models. In photos, I […]

While we wait for Apple's rumored foldable iPhone, Samsung is polishing a design it already has on the market. I just spent three hours with my hands on the new Z Fold 8 Ultra, and all I can think about is how Samsung finally made the crease invisible. Samsung says it achieved this thanks to […]

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.

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

most people know Mistral for its chat models. the part i find more interesting for enterprise work is the Agents API : persistent agents with instructions and tools, stateful conversations you can resume, built-in connectors (web search, code interpreter, document library), and handoffs so one agent can delegate to another. the .net story stops short of this. the community sdks (tghamm's is genuinely good) cover chat completions, embeddings and function calling. they don't cover the agentic layer. so if you're a .net shop that wants to build a multi-agent workflow on mistral, you're writing raw http. i didn't want to, so i built Mistral.Agents.Net . here's the design and the one wire-format detail that cost me a debugging session. agents, not just completions a chat completion is stateless: you send messages, you get a reply, you manage all the history yourself. an agent is a stored object with instructions and tools, and a conversation is a stateful thread you can continue by id. that difference matters for enterprise workflows, where a "session" spans many turns and you want the platform to hold the state. var agent = await client . CreateAgentAsync ( new CreateAgentRequest { Model = "mistral-medium-latest" , Name = "Financial Analyst" , Instructions = "Use the code interpreter for math and web search for current facts." , Tools = { AgentTool . CodeInterpreter (), AgentTool . WebSearch () }, }); using var turn = await client . StartConversationAsync ( new StartConversationRequest { AgentId = agent . Id , Inputs = "what was 15% of last quarter's revenue if it was 12.4M?" , }); Console . WriteLine ( turn . OutputText ); the response isn't a single message. it's a list of outputs: tool executions, message chunks, function calls, handoffs. the library gives you OutputText for the common case and Outputs for the raw stream, plus a Root JsonElement escape hatch for anything the typed model doesn't cover yet. same philosophy i used for the serpapi and elevenlabs clients:

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