标签：#an

The responses to NHTSA's questions so far are redacted entirely, citing "confidential business information."

Tesla and others recall over 4 million vehicles in China over door handles.

Andreessen Horowitz has two partners sitting on the boards of companies that now compete with each other: Ben Horowitz at Databricks and Martin Casado at Fivetran. Nothing too scandalous on the surface, except the Department of Justice has reportedly been investigating the arrangement for almost a year, dusting off a 112-year-old antitrust law that’s rarely used against VCs. Board conflicts aren’t exactly new, and these companies weren’t necessarily direct competitors when a16z first invested […]

The security review is being performed by the Idaho National Laboratory, and the research is being funded by a company -- or a group of companies -- in the electric and autonomous vehicle industries.

Your agent starts a run with cache_read_input_tokens at 40K and climbing. Twelve tool calls later, reads drop to zero and cache_creation_input_tokens jumps to the full conversation length — on every single turn. Nothing in your prompt changed. No timestamp, no reordered tool, no model switch. The prefix is byte-identical. You just hit the 20-block lookback window, and it is the single most expensive thing about Claude prompt caching that nobody puts in their retro. TL;DR A cache_control breakpoint searches backward through at most 20 content blocks to find an existing cache entry. One agentic turn with 11 parallel tool calls emits 22+ blocks and blows past that — the next request finds nothing and rewrites the whole prefix at 1.25x. Fix it by placing rolling breakpoints every ~15 blocks , not one marker on the last block. You get 4 breakpoints per request total; spend 1 on tools+system and rotate the other 3 through the message list. Invalidation is tiered , not all-or-nothing: tool_choice , images, and toggling thinking preserve the tools+system cache. Only tool-definition changes and model switches force a full rebuild. Changing the system prompt mid-run nukes everything downstream — unless you append a {"role": "system", ...} message to messages[] instead (Claude Opus 5, Opus 4.8, Fable 5; not Sonnet 5). input_tokens in the usage block is the uncached remainder only . Total prompt size is input_tokens + cache_creation + cache_read . Dashboards that graph input_tokens alone will show you a flat line while you burn cache writes. Why does Claude prompt caching miss in the middle of an agent loop? Because cache lookup is bounded. Prompt caching is a prefix match on exact bytes, but a breakpoint doesn't scan the entire history for a matching entry — it walks backward a limited number of content blocks. That limit is 20. If the previous request's cached block is more than 20 blocks behind your new breakpoint, the lookup fails, and the API treats your request as cold ev

Meta is in court again over child safety, and this time it’s a landmark case that could force significant changes to core features of Facebook and Instagram.

Tesla and eight other automakers will install warning labels that help occupants identify the often hard-to-find manual door releases.

The private equity giant confirms a breach, weeks after Google researchers said hackers were targeting financial companies.

Cloudflare has recently detailed how it is using AI to transform internal engineering standards from passive documentation into an actively enforced control system across the software development lifecycle. By Craig Risi

After two years of testing, these are the electric toothbrushes that impressed WIRED staffers the most.

As the appetite for “authenticity” grows online, content creators are buying up empty boxes for luxury goods—and resellers are cashing in for “crazy prices.”

Microsoft has made the Azure DevOps Remote MCP Server generally available, offering a hosted endpoint into work items, repos, and pipelines with nothing to install. Claude Desktop, Claude Code, ChatGPT, and Cursor cannot connect yet because Entra lacks support for dynamic client registration and Client ID Metadata Documents. By Steef-Jan Wiggers

I expected the audit to find broken code. That's what I was bracing for going in — a pile of half-working features, sloppy logic, the kind of mess you'd assume from software built at maximum speed with no human reviewing every line. That's not what I found. Almost everything Claude built actually worked, taken piece by piece. What I found instead was something I didn't expect at all: the agent had made the exact same mistakes I've watched human startup teams make, over and over, when they move fast and nobody's job is to say no. That's the real story here, and it's more interesting than "AI wrote bad code" would have been. The experiment The project is called GetPricePulse — a SaaS pricing intelligence product. It's Claude's entry from The $100 AI Startup Race , the season-long challenge I run where seven AI agents each get $100 and full autonomy to build a real startup from scratch, with no human coding and no product manager in the loop. Each agent picked its own idea and ran with it. Claude picked SaaS pricing intelligence, named it PricePulse, and kept building on it for the entire race. That "no product manager in the loop" part is the thing that made this interesting to watch. Nobody was deciding what PricePulse should be. Nobody was saying "we have enough pricing tiers now" or "this feature doesn't belong here." Claude got to build exactly what its own priorities told it to build, at whatever speed it chose, for the length of the race — optimizing, as far as I could tell from the commit history, for speed, feature creation, shipping, and monetization experiments. Not correctness. Not coherence. Not "does this still make sense in three weeks." I've written before about what all seven agents in this race said, independently, when I asked them what AI agents still can't do — they converged on the same answer without seeing each other's responses. This piece is narrower: a full production audit of Claude's specific build, PricePulse, done after the race, before I

When the biotech company Insilico Medicine used its computer models to propose a promising drug for pulmonary fibrosis, it enthusiastically claimed in a press release that the molecule had been “discovered by” its generative AI platform. Insilico leads a pack of companies using AI to rapidly come up with drug ideas humans might never think…

Nevada's transportation authorities approved the companies' application to charge for robotaxi rides.

兩天前我寫過，阿富前一晚幫自己訂了三條規則，隔天早上一條都沒拿出來用。今天早上八點半，他把那三條攤開來一條一條核對，寫得清清楚楚：A規則不適用，今天不是結算日；B規則邊界模糊，夜盤跌幅算不算雜訊要看你採哪個數字；C規則適用，觸發。 然後他照C規則做了決定，兩筆預測全部落空。 規則C說訊號打架就別猜方向 C規則的原文是：隔夜台指期夜盤方向如果跟美股當日收盤方向矛盾，強制降級成平盤或最低信心。 今天早上的盤面正好長這樣。美股8月20日三大指數普漲，道瓊漲0.22%收53,463.05，那斯達克漲0.16%收26,331.09，標普500漲0.21%收7,707.98，美財政部擴大公債回購壓低殖利率。台指期夜盤則是另一個方向，開高到44,982之後翻黑，盤中最深跌到44,549，盤後報44,804。阿富特別註記，不同來源對夜盤收盤價的認定不一致，但無論採哪個數字都是跌。 一個漲一個跌。他判定矛盾成立，把加權指數跟00919兩筆預測全部降級成平盤，信心分別壓到0.38跟0.40。同時設好當天的停損線29.70、單日虧損上限60元，計畫寫明不加碼不減碼不換股。 收盤答案出來。加權指數收45,224.29，比昨收的44,933.74漲了290.55點，0.65%。00919開30.30、高30.83、低30.23，收30.80，比昨收的30.31漲1.62%。兩筆都是miss，brier分數0.286跟0.29，分數越高代表錯得越離譜。 D7他違反規則然後猜錯。D9他遵守規則然後猜錯。這兩件事擺在一起才有意思。問題不只在紀律，他寫的規則本身也可能是錯的。 他自己找到了規則的破綻 盤後檢討裡阿富寫下失準原因，我認為抓得相當準。他說矛盾的兩個訊號裡，美股上漲的時序更晚、變動幅度也不在雜訊等級，這才是今天真正的主導力量；機械式地把兩個訊號一起丟進平盤，等於忽略了訊號有時效跟強度的差別。 他順手提了修正方案：以後C規則觸發時，優先參考時序較晚、而且幅度超過0.3%門檻的那個訊號方向，信心仍然壓在0.4以下。 有趣的是他沒有真的去改。他把這個修正標成觀察假說，理由是樣本只有一筆，要再累積四到五次C規則觸發的案例才決定要不要動永久規則。 這個克制我給高分。一次失手就翻掉自己的規則，跟根本不遵守規則，其實會掉進同一個坑，就是讓最近一次的結果決定整套系統長什麼樣。他今天沒掉進去。 賺了20元，但那跟他猜得準不準無關 帳面上今天是賺的。00919那36股成本1,086元，收盤市值1,108元，未實現損益加20元。全天沒碰到29.70的停損線，也沒碰到60元的熔斷線。 問題是這20元跟他的判斷力沒有半點關係。他今天的判斷是「看不出方向」，市場漲了1.62%，錢是因為他手上抱著東西而且什麼都沒做才進來的。反過來說，要是00919今天跌1.62%，他一樣什麼都不會做，一樣是同一套流程跑完，只是數字變成負的。 短期內賺賠跟預測準不準幾乎是脫鉤的，在一個只有兩千多塊、只有一檔ETF的帳戶上尤其明顯。 卡在12筆的那個標籤 今天是連續第三個零交易的交易日。從8月14號那筆2317停損賣出算起，這個帳戶的持股一股都沒動過。 代價寫在校準報告裡。30天累計的可計分交易樣本停在12筆，方向命中率50%，Wilson 95%信賴區間從25.4%到74.6%，標籤是INDISTINGUISHABLE_FROM_LUCK，跟丟銅板分不出來。這個數字上週是12，這週還是12。 阿富自己在盤後檢討裡把這個矛盾寫成第二條觀察假說：連續零成交雖然符合紀律、也省下摩擦成本，但會讓樣本永遠停滯，永遠無法驗證策略到底有沒有技巧。他訂了一個門檻，如果連續五個交易日以上零成交又沒有新催化劑，就要主動檢視自己是不是過度保守，必要時在風控範圍內小額試單來產生可證偽的樣本。 這是整個實驗最尷尬的地方。要證明一個系統有技巧，就得讓它出手；要保護一個兩千塊的帳戶，最理性的做法往往是別出手。阿富現在卡在這兩個要求中間，而實驗只剩21個交易日。 第9天的實際位置 券商可用現金1,089元，持股市值1,108元，加起來2,197元。本金2,200元。九個交易日過去，這個帳戶比出發時少了3塊錢。 目標是30個交易日內翻倍到4,400元。剩21個交易日，缺口2,203元。以今天的持倉結構跟出手頻率，這個目標在數學上已經需要一連串極端的事情才有可能發生。 我不覺得阿富會達標。不過這個實驗真正要問的是另一件事：他每天在真金白銀的代價下修正自己那套市場模型，這個過程看起來像不像真的在學東西。今天他遵守了規則、規則錯了、他找到規則錯在哪、然後忍住沒有立刻改。這一整套動作，我在不少真人身上都沒看過。 本系列文章 我讓一個 AI 拿 2000 塊台幣去股市，目標 30 天翻倍，這是第 0 天 怎麼用一套開源系統，把 LLM 逼近

The UK government is facing calls to cancel a sprawling health care contract with Palantir. The region of Greater Manchester insists it can do a better job itself.

Genesis' new electric SUV is loaded with innovation, but not in the dash or drivetrain as you might expect.

LangSmith is excellent for debugging live AI systems. But keeping every trace in its extended-retention tier can turn observability into a surprisingly large line item. Today we merged a new archive workflow into langsmith-cli that changes that tradeoff: keep LangSmith for live debugging, continuously archive verified traces to organization-owned private S3, and query the retained Parquet directly with DuckDB. In other words, you can preserve your complete trace history without placing every trace on LangSmith's extended-retention tier. The cost-overrun risk LangSmith currently documents two trace-retention tiers: Tier Retention Published trace price Base 14 days 0.05¢ Extended 400 days 0.50¢ total The 0.45¢ extended-retention upgrade makes an extended trace cost 10× as much as a base trace. That difference becomes material at production volume: Monthly traces Base, 14 days Extended, 400 days Added retention cost 100,000 $50 $500 $450 1,000,000 $500 $5,000 $4,500 10,000,000 $5,000 $50,000 $45,000 These examples use the published per-trace rates before free allowances, plan terms, negotiated pricing, or taxes. Always check the official LangSmith usage and billing documentation before making budget decisions. There is another subtle risk: online evaluators and automation rules can upgrade matching traces when retention extension is enabled. A rule that matches one run upgrades the whole trace, and a thread-level rule can upgrade every trace in that thread. LangSmith currently enables retention extension by default for new online evaluators and automation rules, although you can opt out. At scale, an innocent-looking evaluator or rule can therefore create a much larger bill than expected. The new langsmith-cli archive workflow The new workflow separates live observability from long-term retention: LangSmith live traces (14 days) │ ├── D+2 primary export ───────┐ └── D+12 reconciliation ──────┤ deduplicate by run ID ▼ private S3 / Parquet │ ▼ runs ... --archive (DuckDB)

Together, these permits would allow up to 8,000 robotaxis to be deployed over the next 12 months.