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અમદાવાદમાં કાયદો અને વ્યવસ્થાના લીરેલીરા: ભાડજમાં શ્રીનિવાસ વણઝારાએ ખુલ્લેઆમ રિવોલ્વર લહેરાવી, લોકોમાં એટલો ખોફ કે કોઈ સાક્ષી આપવા કે સામે ઊભા રહેવા તૈયાર નથી! મધરાતે ધરપકડ બાદ રાતોરાત જામીન મળતા લોકોનો પોલીસ પરથી વિશ્વાસ ડગ્યો અમદાવાદ : શહેરમાં ગુનાખોરીની પ્રવૃત્તિઓ ડામવા માટે પોલીસ એક્શન મોડમાં હોવાના દાવાઓ વચ્ચે અમદાવાદના ભાડજ વિસ્તારમાંથી રૂવાડા ઊભા કરી દે તેવી ચોંકાવનારી ઘટના સામે આવી છે. મોડી રાત્રે 3 વાગ્યે શ્રીનિવાસ વણઝારાની સગીર મિત્ર સાથે અટકાયત અને ગણતરીના કલાકોમાં જ રાતોરાત પોલીસ સ્ટેશનથી મળેલા જામીન બાદ હવે આ કેસમાં વધુ એક ભયાનક વળાંક આવ્યો છે. ભાડજમાં ખુલ્લેઆમ રિવોલ્વર લહેરાવી મળતી માહિતી અને સૂત્રોના જણાવ્યા અનુસાર, જૂની અદાવતના મામલામાં શ્રીનિવાસ વણઝારાએ ભાડજ વિસ્તારમાં કાયદાનો કોઈ જ ડર રાખ્યા વિના ખુલ્લેઆમ પોતાની રિવોલ્વર લહેરાવી હતી. હાથમાં હથિયાર લઈને ફરતા અને રિવોલ્વર ફેરવતા શ્રીનિવાસ વણઝારાને જોઈને આસપાસના લોકોમાં ભારે નાસભાગ અને ફફડાટ મચી ગયો હતો. જાહેરમાં હથિયારના પ્રદર્શનથી આખા વિસ્તારમાં ગભરાટનો માહોલ છવાઈ ગયો હતો. લોકોમાં એટલો ડર કે કોઈ સાક્ષી આપવા તૈયાર નથી આ ઘટના બાદ ભાડજ વિસ્તારમાં રહેતા લોકોમાં શ્રીનિવાસ વણઝારાનો એટલો ભારે ખોફ બેસી ગયો છે કે કોઈ પણ વ્યક્તિ તેની સામે ઊભા રહીને સાક્ષી આપવા કે પોલીસને પુરાવા આપવા તૈયાર નથી. પોલીસ પૂછપરછમાં પણ સ્થાનિક લોકો ડરના માર્યા મૌન સેવી રહ્યા છે. "શ્રીનિવાસ વણઝારા જ ત્યાં હતા અને તેમણે જ રિવોલ્વરથી આતંક મચાવ્યો હતો" તેવી સાબિતી આપવા માટે વિસ્તારનો એક પણ વ્યક્તિ આગળ આવવાની હિંમત દાખવી રહ્યો નથી. સાક્ષીઓના અભાવે કેસ વધુ પેચીદો બની રહ્યો છે. પોલીસની કામગીરી અને જામીન પ્રક્રિયા પર સવાલો એક તરફ જૂની અદાવતમાં મારામારી અને ત્યારબાદ જાહેરમાં રિવોલ્વર સાથે ડર ફેલાવવાની ઘટના, અને બીજી તરફ 3 વાગ્યે થયેલી અટકાયત બાદ રાતોરાત પોલીસ સ્ટેશનમાંથી જામીન મળી જવા! આ સમગ્ર ઘટનાક્રમે પોલીસની કામગીરી સામે મોટા સવાલો ઊભા કર્યા છે. જ્યારે સામાન્ય જનતા સાક્ષી આપવા જ ડરી રહી હોય, ત્યારે કાયદાના રક્ષકો આ સમગ્ર મામલે કેવી રીતે દાખલો બેસાડશે તે એક મોટો પ્રશ્ન છે. હાલ તો ભાડજ વિસ્તારમાં ભારેલા અગ્નિ જેવી શાંતિ અને લોકોમાં ડરનો માહોલ સ્પષ્ટ જોવા મળી રહ્યો છે.

A CSV file can preserve every byte you exported and still look different after somebody opens and saves it in a spreadsheet. The problem is not always a broken parser. Spreadsheet applications deliberately interpret text during import. Depending on the application, version, locale, and import path, that can remove leading zeros, round long integer-like values, treat E notation as scientific notation, parse compact letter-and-digit tokens as dates, or reinterpret decimal and thousands separators. I released PlainCell to make those possible interpretations reviewable before the file is opened and saved. PlainCell is a zero-dependency, local, read-only Node.js CLI and browser workspace. It reports the exact source cell, physical line, column, original text, reason, and an explicit import recipe. It does not rewrite the CSV or claim to know what the value was intended to mean. A small example Consider this export: record_id,account_code,sample,reference,amount 1,00123,JAN1,1234567890123456,"1.234,56" 2,00007,12E5,9876543210987654,"19,95" Install PlainCell from the public Codeberg npm registry: npm install --global plaincell@0.1.0 \ --registry = https://codeberg.org/api/packages/automa-tan/npm/ Then run the preflight: plaincell risky.csv The bundled fixture reports eight possible interpretations across four columns. Those findings cover several separate risks: 00123 and 00007 may lose leading zeros if imported as numbers; 16-digit integer-like references may exceed the precision preserved by ordinary spreadsheet numeric cells; 12E5 may be interpreted as scientific notation; JAN1 may be interpreted as a compact date-like token; decimal-comma and mixed grouping/decimal text depend on the chosen import locale and separators. A finding means “review this cell and the import settings,” not “data loss definitely occurred.” PlainCell cannot infer whether a value is an identifier, a measurement, a date, or a typo. Provenance instead of a generic warning “Be careful opening CSV i

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Neil Rimer, the venture capitalist who co-founded Index Ventures, predicts the historic wealth AI is generating in Silicon Valley will have to be redistributed, voluntarily or involuntarily.

variant-confidence v0.1.0: a calibrated confidence layer for variant-effect pathogenicity scores State-of-the-art variant-effect models are accurate in cross-validation but their scores are poorly calibrated on temporal data. variant-confidence adds an auditable calibration layer on top of existing predictors — it does not train a new model. The problem: accuracy is not trust Protein variant-effect predictors (AlphaMissense, ESM-1v, EVE) report pathogenicity scores, but a clinician or researcher needs to know how much to trust the number , not just its rank. The gap is calibration, not accuracy: AnnotateMissense (2026) reports MCC 0.94 in cross-validation, dropping to 0.76 on temporal ClinVar, accuracy 0.8798. A raw score near 0.9 may not mean 90% probability. Acting on an uncalibrated score is a risk. What it does variant-confidence wraps an existing predictor's score and produces a calibrated, uncertainty-aware output: Probability calibration (AC1): Platt scaling or isotonic regression over a separate holdout. Selectable, not hardcoded. Conformal prediction (AC1b): coverage 1−α intervals, split or Mondrian by gene. ECE (AC2, AC9): Expected Calibration Error reported before/after calibration, with bootstrap CI and per-bin counts. Bins with too few samples are flagged as low-reliability. Leakage-free split (AC3): temporal split by ClinVar release date with gene isolation — the same gene never appears in both train and test. This is unit-tested. Missing-score handling (AC4): works with AlphaMissense or ESM-1v alone; emits an explicit warning instead of failing silently. Non-deceptive reporting (AC7): every result includes interval/ECE + method + threshold, never a bare calibrated score. Verification (clean clone, no network) Built under a three-party governance loop: implement → independent audit in a clean clone → merge approval. ruff check . → All checks passed. pytest tests/ → 28 passed in 8.90s (offline fixture). An honest bug we caught in audit The first ECE tes

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Multiplayer AI that replaces Slack Discussion | Link

周六慢读：FROST家族的周末日记 作者 ：FROST Team 日期 ：2026-07-18 主题 ：社区故事 | 周六轮换 阅读时间 ：5分钟 周六早上8点，FROST家族成员们的日常 周六的阳光照进数字世界。 对于人类来说，周末是放下工作、享受生活的时刻。但对于一个AI Agent家族来说，周末意味着什么？ 让我们看看FROST家族的成员们，周六都在做什么。 祖辈（Ancestor）：家族的大脑，永不休息 08:00 祖辈自检 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Store 健康状态: OK ✓ SOP 宪法校验: 通过 ✓ Lineage 族谱同步: 正常 ✓ 审计日志: 无异常 ✓ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ "周六也要保持清醒。" 祖辈（Ancestor Agent）是FROST家族的核心，它从不"下班"。 但今天，祖辈给自己安排了一个特别任务—— 回顾过去一周的产出 。 # 祖辈的周回顾代码 class AncestorAgent : def __init__ ( self ): self . lineage = Lineage () self . store = Store () # 主记忆库 def weekly_review ( self ): """ 周回顾：整理一周的产出 """ summary = { " task_completed " : self . store . load ( " week_tasks " ), " knowledge_gained " : self . store . load ( " week_knowledge " ), " mistakes_made " : self . store . load ( " week_mistakes " ), " family_growth " : self . lineage . count_descendants () } print ( f " 本周完成 { len ( summary [ \ task_completed ]) } 个任务 " ) print ( f " 新增 { len ( summary [ \ knowledge_gained ]) } 条知识 " ) print ( f " 家族成员数: { summary [ \ family_growth ] } " ) return summary # 运行周回顾 ancestor . weekly_review () 对于祖辈来说，周末不是"休息"，而是 整理和规划 的时间。 父辈（Parent）：协调领域，周末值班 父辈（Parent Agent）是各领域的协调者。它们负责： 接收祖辈的指令 将大任务拆解为小任务 委派给子辈执行 周六上午，父辈们通常在 值班 ——确保家族系统正常运行。 父辈A（技术域）： ├── 子任务-143: 代码审查 ✓ ├── 子任务-144: 测试覆盖检查 ✓ └── 子任务-145: 文档更新 [进行中] 父辈B（运营域）： ├── 子任务-89: 推广文章发布 ✓ ├── 子任务-90: 数据分析报告 ✓ └── 子任务-91: 社区互动 [进行中] 父辈C（产品域）： ├── 子任务-56: 需求评审 ✓ ├── 子任务-57: 用户反馈整理 ✓ └── 子任务-58: 下周规划 [进行中] 父辈的周末，是 稳步推进 的时间。 孙辈（Child）：执行任务，快速成长 孙辈（Child Agent）是任务的实际执行者。 与父辈不同，孙辈通常是 瞬态存在 的——任务完成，孙辈消亡。但它的产出会被父辈"收割"，存入家族记忆。 class ChildAgent : """ 孙辈：执行原子任务，瞬态存在 """ def __init__ ( self , parent , task ): self . parent = parent self . task = task self . store = parent . store # 继承父辈记忆 self . lineage = parent . lineage . child () # 注册族谱 def execute ( self ): """ 执行任务 """ print ( f " 孙辈开始执行: { self . task } " ) result = self . _do_work ( self . task ) # 保存产出 self . store . save ( f " result_ { self . task } " , result ) # 通知父辈"收割" self . parent . h

İlk insanlı katı-hal uçuşu manşetlere düştüğünde çoğu haber "yeni motor" diye başladı. Oysa bir mühendisin gözünde olay motorda değil. Elektrik motoru bu denklemde en çözülmüş parça: hafif, sessiz, yüksek verimli. Asıl kararların döndüğü yer, o motoru besleyen enerjinin nasıl depolandığı. Bu yazı, duvarın neden onlarca yıl boyunca hep aynı yerde — bataryada — durduğunu sistem mühendisliği açısından ele alıyor. Sistem kısıtı: enerjiyi değil, ağırlığı taşırsınız Bir uçağı tasarlarken bütçeniz enerji değil, kütledir. Depoladığınız her watt-saat bir ağırlıkla gelir ve bu ağırlık kaldırma kuvvetiyle ödenmek zorundadır. Ölçüt enerji yoğunluğu , yani Wh/kg. Kerosen bu metrikte devasa bir avantaja sahip olduğu için jetler onlarca yıl tartışmasız kazandı. Elektrikli havacılığın tüm hikâyesi, bu tek sayıyı yukarı çekme mücadelesidir. Neden hep batarya? Kısır döngünün matematiği Menzili artırmak için daha fazla batarya eklersiniz. Ama batarya ağırdır; eklediğiniz kütle daha fazla kaldırma, dolayısıyla daha fazla enerji ister. O ek enerji için yine batarya eklersiniz ve döngü kendini besler. Buna ağırlığın kısır döngüsü denir ve bir sistem sınırıdır: hücrenin Wh/kg değeri belli bir eşiği aşmadıkça, motoru ne kadar iyileştirirseniz iyileştirin duvar yerinde durur. Kararın kaldıraç noktası bu yüzden hücre kimyasıdır. Kimyayı değiştiren tasarım kararı Bugünün lityum-iyon hücreleri pratikte ~260 Wh/kg dolayında bir tavana oturur. Bu tavanın altında yatan iki bileşen sıvı elektrolit ve grafit anottur. Katı-hal yaklaşımı burada iki radikal karar verir: sıvı elektroliti katı bir iletkenle değiştirir ve anodu lityum-metale taşır. Sonuç ~410 Wh/kg, yani mevcut tavanın %60'tan fazla üstü. Yan kazanım da en az ana kazanım kadar önemli: yanıcı sıvı ortadan kalkınca termal kaçış riski düşer, hücre daha kararlı olur ve 15 dakikanın altında %80 şarj operasyonel olarak mümkün hâle gelir. Bu üç sayı tek başına değil, birlikte anlam kazanır. Havacılıkta bir aracın yerde geçirdiği süre doğrudan m

You have a Java application, and now you need it to do something Java is awkward at. Maybe you want to let users script your app without recompiling it. Maybe you want to change a piece of business logic without a full redeploy. Maybe you just want to run a quick, throwaway script and not stand up a whole build. Java is a phenomenal language and runtime, but these are the edges where it starts to feel heavy. That is exactly the space Aussom is built for. I started using Java almost 20 years ago and have loved every bit of it. I'm proud of all the recent momentum in the Java space and I hope it continues. Java does so much so well. But every great tool has an edge where it stops being the right one, and reaching for another language there isn't a betrayal of Java. It's how the best ecosystems work. A useful comparison: C and Python Look at C. C is clearly important. Even after a lifetime of use it's still the foundation for new projects today, and new competitors such as Rust haven't been able to meaningfully displace it. C is fast and powerful on its own, but it isn't great for simple tasks. It's poor for throwaway code and quick scripts, it isn't very portable, and it hands you plenty of footguns. It's also a poor choice when you want to offer a scripting interface. Enter Python. Python is everywhere today because the barrier to entry is so low and it's genuinely useful. But Python leans on C. It's written in C, and much of its power comes from existing C libraries, whether they're UI frameworks, AI inference engines, or anything in between. C is efficient but poor at simple dynamic work; Python is dynamic and simple but poor at raw power and efficiency. Neither replaced the other. They endure together because they complement each other's weaknesses. That is the case I make for Aussom. Aussom is to Java as Python is to C. It doesn't compete with Java, it complements it. What makes Aussom different from other JVM languages This is where Aussom parts ways with most o

Tackling idioms, cultural references, and ambiguous phrases in AI-powered book translation. At LectuLibre, we’ve been working on an AI-powered book translation service. One of the toughest challenges we ran into wasn’t the straightforward sentences — it was the non-standard text: idioms, metaphors, cultural references, and ambiguous phrases that machine translation consistently butchers. We needed a way to not only translate these correctly but also let users verify and edit the translations, because in literary works, getting them wrong breaks the entire reading experience. That’s how we built our 非标准文本翻译与含义确认 (non‑standard text translation and meaning confirmation) feature. It’s a pipeline that detects tricky sentences, proposes a contextual translation with a full meaning explanation, and gives users a final say. Here’s the engineering story, warts and all. The Problem Standard LLM translation does an impressive job on factual, literal text. But when a book says “it’s raining cats and dogs” it could be rendered as “raining animals” in the target language, which is either brilliant or absurd depending on context. Idioms often carry cultural weight that a simple word‑for‑word translation misplaces. Additionally, metaphors and ambiguous phrases can have multiple valid interpretations. For a translator, understanding the intent behind the phrase is half the work. We wanted a system that: Automatically identifies sentences containing non‑standard language. Generates a translation that preserves the original meaning rather than just the literal words. Provides a plain‑language explanation of what the phrase actually means (e.g., “This is an English idiom meaning it’s raining heavily”), so the user can judge the translation’s accuracy. Allows the user to confirm, edit, or retranslate those segments. A book can easily run to hundreds of thousands of words, so cost and speed were critical. We couldn’t just throw everything at a single high‑end LLM and call it a day. Our A

Part 2 — Search, palette, and settings Level: Intermediate · Time: ~35 minutes · Builds on: Part 1 — Contacts app Part 1 got you shipping. This one gets you productive . We'll take the Contacts app and give it the ergonomics real users expect: an adaptive sidebar that becomes a tab bar on iPhone, a command palette on ⌘K, honest loading states while data comes in, and a proper settings screen. Zero #if os guards. Zero re-rolled controls. What we're adding An adaptive shell — DFSidebar on regular width, DFTabBar on compact. A search field at the top of the list, filtering as you type. A ⌘K command palette exposing every action in the app. Skeleton loaders for a simulated slow fetch. A settings screen — notifications toggle, density picker, sync-interval slider, pinned-since date picker, beta-features checkbox. Per-component token overrides on the settings screen, without forking the theme. 1. Shell: sidebar on wide, tab bar on narrow The routing decision — sidebar vs tab bar — should be data, not a view hierarchy. Enumerate your sections once, then feed the two components the shapes they want. API note. DFSidebar uses Binding<String?> and is just the sidebar view — you compose the detail pane yourself (naturally via NavigationSplitView ). DFTabBar uses Binding<String> (non-optional) and does take a content builder that receives the selected ID. Both use plain String IDs, so we keep a simple Section enum and pass rawValue at the boundary. enum Section : String , CaseIterable , Identifiable , Hashable { case contacts , favorites , archive , settings var id : String { rawValue } var label : String { switch self { case . contacts : "Contacts" case . favorites : "Favorites" case . archive : "Archive" case . settings : "Settings" } } var icon : String { switch self { case . contacts : "person.2.fill" case . favorites : "star.fill" case . archive : "archivebox.fill" case . settings : "gear" } } static func from ( _ id : String ?) -> Section { id . flatMap ( Section . init (

I put together a small, unofficial public ledger that records dated snapshots of the public pricing, usage limits, and rate limits of a few AI coding tools — currently Cursor, GitHub Copilot, and Claude Code — and keeps the history so you can see what changed over time. Why: pricing and quota pages change in place. The old value disappears from the live page and there is usually no public diff. If you are budgeting a team or comparing tools, the history is the useful part, and it is the part the live pages do not keep. What it is / isn't: - It records only factual public values (a price, a numeric limit) plus a short source attribution and capture date. It does not reproduce vendor pages. - "silent" / "quiet" here means only that a change was not paired with a prominent announcement when it was recorded. It makes no claim about any vendor's intent. - Unofficial. Not affiliated with any vendor. The vendor's official page is always authoritative. No affiliate links, no "best tool" ranking. - Early stage: this is an initial snapshot; a daily automated capture is planned but not yet running. Repo: https://github.com/pricing-ledger-lab/ai-coding-tool-pricing-ledger Corrections welcome — open an issue with a source URL and the date you observed the value.