找到 1980 篇相关文章
As Chinese AI models grow in capability and popularity among U.S. companies, the arguing over what should be done about them has reached a fever pitch.
A mirror that moves mid-photon could release a shower of new photons.
How to choose between two of the most notable flip phones on the market.
a16z Speedrun, Ada Ventures, and Snowball VC have invested in Cascade's $3.5 million seed round.
We’ve compiled an overview of some of the top alternative browsers available today aiming to challenge Chrome and Safari.
Samsung's smartphone lineup pits a jack of all trades against a master of one.
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.
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 […]
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
Jake Mannix discusses moving AI agents past chaotic "1970s BASIC" architectures. He shares how implementing an intermediate protocol layer allows engineering leaders to build versioned, encapsulated "virtual tools." This design enables interface mapping, dynamic schema projection, and runtime taint tracking to proactively eliminate data exfiltration risks without slowing velocity. By Jake Mannix
Glow is targeting a new class of endpoint risks created by the rapid adoption of AI agents and developer tools inside enterprises.