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Cash App’s launching a phone service

Cash App's AT&T-based MVNO will offer an unlimited 5G data plan for $40 per month including taxes and fees. The new mobile service is powered by Gigs, the same firm behind the Klarna mobile service that launched last year with the same pricing and is "rolling out to select users, with broader availability planned in […]

2026-06-11 原文 →
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A free model that runs 4x faster on your own GPU — and two more shifts for builders

A free model that runs 4x faster on your own GPU — and two more shifts for builders Three things landed for builders at once: a free open model that generates text far faster, a more autonomous Codex, and Anthropic owning up to a model that was quietly holding back. Two of them you can act on right now. Here's the 2-minute video version if you want the quick pass first: 1. Google shipped DiffusionGemma — a free open model that runs 4x faster Google released DiffusionGemma , an open-weights model that uses text diffusion instead of standard autoregressive decoding. Instead of generating one token at a time, it generates whole blocks in parallel. It writes blocks of 256 tokens at once , for up to 4x faster generation on a dedicated GPU. It hits 700+ tokens per second on a single RTX 5090 , and fits in 18GB of VRAM quantized — inside consumer GPU limits. It's a 26B Mixture-of-Experts (only 3.8B parameters active), ships under Apache 2.0 , and runs natively in vLLM . The tradeoff Google states openly: output quality is lower than standard Gemma 4, so it's a speed play, not a quality play. Why it matters: this is a fast, free, local draft model you can run on your own hardware. Use it for low-latency drafts and agent loops, then route the hard calls to a stronger model. No inference bill for the cheap 80%. 2. OpenAI gave Codex web search and autonomous goals OpenAI shipped a major Codex update that pushes it further toward an autonomous agent. Code mode can now call web search directly , even from nested JavaScript tool calls — so it can look up current API docs mid-implementation. Goal mode is generally available across the Codex app, the IDE extension, and the CLI. Appshots (macOS) attach an app window to a Codex thread with a hotkey, and MCP tool schemas now preserve oneOf / allOf for richer connectors. Why it matters: Codex can research and chase a goal on its own across every surface. Still — hand it a clear, scoped goal in a branch. Full hand-offs go sideways witho

2026-06-11 原文 →
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An LLM benchmark is only useful for as long as it's hard

The general shape of the problem is that every public LLM benchmark is on a saturation clock that runs from the moment of its publication to the moment a model's training corpus has eaten it. The clock has been running, on the visible benchmarks of the last five years, for somewhere between twelve and thirty months before each one is no longer useful for differentiating frontier models. The benchmarks are not failing. They are doing exactly what they were designed to do, in the order they were designed to do it, and the field has been running through them faster than the people designing them anticipated. I want to put numbers on the saturation pattern, walk through what the contamination evidence actually says, and then sit with the question of what an honest benchmark would have to look like in 2026 — because the "private held-out eval" answer that the labs are converging on has economics that are worth examining carefully before any of us salute it as the solution. The saturation timeline, with numbers HumanEval (Chen et al., OpenAI, July 2021). 164 hand-written Python problems. The benchmark was published with Codex at 28.8% pass@1; the underlying GPT-3 base model scored 0%. GPT-4 (March 2023) hit 67% in the original Technical Report. By late 2024, OpenAI's o1-preview and o1-mini both reached 96.3% pass@1 ; Claude 3.5 Sonnet sat at 93.7%. The benchmark is saturated in the operational sense — the relative spread across the top ten models is around 10 percentage points, which is too small a gap to differentiate them on, and most of the new models arrive within a percentage point or two of the ceiling. The successor variants (HumanEval+ from EvalPlus, with augmented test cases) are the field's response. Lifespan from publication to operational saturation: about 36 months. MMLU (Hendrycks et al., September 2020). 57 subjects, ~14,000 multiple-choice questions, taken from publicly-available test prep and academic sources. The problem with MMLU is not that it's satura

2026-06-11 原文 →
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