Exclusive | Caltech Researchers Claim Radical Compression of High-Fidelity AI Models - WSJ
Exclusive | Caltech Researchers Claim Radical Compression of High-Fidelity AI Models WSJ
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LinkedIn is scanning your browser extensions: what AI developers need to know
LinkedIn is scanning your browser extensions: what AI developers need to know Yesterday, a story hit the top of Hacker News with 1,540 points: LinkedIn is actively scanning your installed browser extensions . For most developers, the reaction was visceral. Not surprised �� but still unsettled. Because this isn't just about LinkedIn. The pattern is everywhere Big Tech products have quietly expanded their data collection to include: Browser extension inventories (LinkedIn) Clipboard contents (TikTok, caught in 2020) Installed app lists (various mobile apps) Keystroke patterns (some "productivity" tools) The AI tools you use every day are no exception. What this means for your AI coding tools If you're using a cloud-based AI coding assistant, consider what telemetry it might collect: ✓ Your f
Google Gemma 4: Everything Developers Need to Know
Google dropped Gemma 4 on April 2, 2026, A full generational jump in what open models can do at their parameter range and the first time in the Gemma family's history that one ships under Apache 2.0, meaning commercial use without permission-seeking. Some context: since Gemma's first generation, developers have downloaded the models over 400 million times and built more than 100,000 variants. Four Models, One Family Gemma 4 is a family of four, each aimed at a different point in the hardware spectrum. E2B : Effective 2 billion active parameters. Runs on smartphones, Raspberry Pi, Jetson Orin Nano. 128K context window. Handles images, video, and audio. Built for battery and memory efficiency. E4B : Effective 4 billion active parameters. Same hardware targets, higher reasoning quality. About
AgentManifest: A Declarative Spec Where the Harness Is the First-Class Decision
RFC v0.3 — design proposal, not a shipping product. CC0 licensed. Feedback and critique welcome. GitHub: MouseRider/agentmanifest-rfc When you run AI agents across more than one role, the execution environment turns out to matter more than it first appears. The model gets most of the attention — benchmarks, leaderboards, capability comparisons — but the harness shapes runtime behavior in ways that model selection alone doesn’t account for. A personal assistant, an ops monitor, a coding agent, a trading bot: these aren’t the same agent with different prompts. They need different memory models, different autonomy levels, different guardrail enforcement, different lifecycle behaviors. Current agent harnesses are mostly either finished platforms you adopt wholesale, or open-ended toolkits that
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Google Gemma 4: Everything Developers Need to Know
Google dropped Gemma 4 on April 2, 2026, A full generational jump in what open models can do at their parameter range and the first time in the Gemma family's history that one ships under Apache 2.0, meaning commercial use without permission-seeking. Some context: since Gemma's first generation, developers have downloaded the models over 400 million times and built more than 100,000 variants. Four Models, One Family Gemma 4 is a family of four, each aimed at a different point in the hardware spectrum. E2B : Effective 2 billion active parameters. Runs on smartphones, Raspberry Pi, Jetson Orin Nano. 128K context window. Handles images, video, and audio. Built for battery and memory efficiency. E4B : Effective 4 billion active parameters. Same hardware targets, higher reasoning quality. About
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【営業マン向け】ChatGPTで商談前の準備を10分で完結する方法
商談前の準備、まだ1時間かけてますか？ AIを使えば10分で終わります。営業歴10年の私が実際に試して効果があったChatGPTの使い方を紹介します。 なぜ商談前の準備にChatGPTが効くのか 商談前に必要な作業は大きく3つ。 相手企業・担当者のリサーチ 想定される質問・反論の準備 提案内容の整理 これを全部手作業でやると1〜2時間かかりますが、ChatGPTを使えば合計10分以内で終わります。 Step1：企業リサーチ（3分） プロンプト例： 「[会社名]という会社について、以下の視点で簡潔にまとめてください。 ・事業内容と収益モデル ・最近のニュースや動向 ・業界での立ち位置 ・競合他社との違い 」 ChatGPTが知っている情報でざっくりまとめてくれます。追加でGoogleニュースを30秒確認するだけで十分です。 Step2：反論・質問の予測（4分） プロンプト例： 「[自社サービス名]を[業種]の企業に提案する際、 担当者から出やすい質問や反論を10個挙げてください。 それぞれに対する返答例も一緒に書いてください。」 このリストを見ながら商談に臨むだけで、焦らず答えられるようになります。 実際に私はアポ率が1.8倍になりました。 Step3：提案の整理（3分） プロンプト例： 「[担当者名]さんは[役職]で、[課題・状況]を抱えています。 私たちの[サービス名]を使ってもらうメリットを、 その方の立場から3点にまとめてください。 数字や具体例を使って説得力を持たせてください。」 相手の立場から考えたメリットが出てくるので、そのまま話のフレームに使えます。 実際に使ってみた結果 商談前準備：2時間 → 10分 アポ率：1.5% → 2.7%（1.8倍） 商談中の焦り：ほぼゼロ 特に反論準備が効きました。「そういえば似たような質問があったな」と思いながら答えると、落ち
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