「insight」の検索結果

September 16, 2026 New ask Hacker News story: Open-sourced jev architecture last year with model,pap

日本語の概要は準備中です。原文の説明を表示しています。

To address this, we propose to recursively generate summaries/ memory using large language models (L

日本語の概要は準備中です。原文の説明を表示しています。

Native 4-bit Activations with Hadamard Transformation for 1-bit LLMs Published in arXiv , 2025 Abstract : Efficient deployment of 1-bit Large Language

日本語の概要は準備中です。原文の説明を表示しています。

Datasets : ProntoQA, FOLIO, ProofWriter, ConditionalQA, StrategyQA Model : GPT-5 (Azure deployment) Config : max_attempts=3 , verify_timeout=10000ms B

日本語の概要は準備中です。原文の説明を表示しています。

August 24, 2026 1 Introduction Reinforcement Learning (RL) has emerged as a highly effective approach for training Large Language Models (LLMs), yet i

日本語の概要は準備中です。原文の説明を表示しています。

Our large-scale experiment with 19 LLMs reveals that current models degrade documents during delegation: even frontier models (Gemini 3.

日本語の概要は準備中です。原文の説明を表示しています。

Self-Retrieval generates relevant passage $p$ using the knowledge embedded within its parameters, which is different from dense retrieval or generativ

日本語の概要は準備中です。原文の説明を表示しています。

MegaTrain Full Precision Training of 100B+ Parameter LLMs on a Single GPU A RAM-centric architecture that stores parameters in host memory and treats

日本語の概要は準備中です。原文の説明を表示しています。

I-A LLMs Large Language Models (LLMs) are extensive, general-purpose models that can be pre-trained and adapted for specific tasks.

日本語の概要は準備中です。原文の説明を表示しています。

F16 on BitNet-embedding-270M prefill (8 threads) Supports I2_S conversion with optimized kernels on x86 CPUs Lossless inference with 2 bits per weight

日本語の概要は準備中です。原文の説明を表示しています。

AshleysBrain 3 hours ago | 9 comments 77 Neovim have a ~$800k Bitcoin donation sitting untouched since 2023 by jakemanger 1 hour ago | 6 comments 741

日本語の概要は準備中です。原文の説明を表示しています。

Navigating Sparse Evidence: Agentic Visual RAG via Explicit Context Selection and Consolidation Soochow University ; Baidu Inc.

日本語の概要は準備中です。原文の説明を表示しています。

Find one insight that eliminates multiple components - "if this is true, we don't need X, Y, or Z

日本語の概要は準備中です。原文の説明を表示しています。

Build features guided by data insights, A/B testing, and continuous measurement using specialized agents for analysis, implementation, and experimentation.

日本語の概要は準備中です。原文の説明を表示しています。

Explain the same design observation to a designer, a developer, and a non-technical stakeholder. Same insight, three languages. Use when working cross-functionally or presenting design decisions to mixed audiences.

日本語の概要は準備中です。原文の説明を表示しています。

Search and analyze Reddit discussions for market research, product feedback, and community insights using Xpoz. Use when asked to "search Reddit", "what does Reddit think about X", "Reddit feedback on X", "subreddit analysis", or "Reddit market research".

日本語の概要は準備中です。原文の説明を表示しています。

Audits Convex performance for reads, subscriptions, write contention, and function limits. Use for slow features, insights findings, OCC conflicts, or read amplification.

日本語の概要は準備中です。原文の説明を表示しています。

Patterns for scaling read-heavy Convex apps to millions of users. Use when optimizing bandwidth, reducing query costs, fixing slow queries, creating digest tables, replacing reactive subscriptions with one-shot fetches, adding compound indexes, debouncing writes, rate-controlling backfills, or running npx convex insights. Trigger when users mention "scale", "bandwidth", "performance", "optimize", "slow queries", "expensive queries", "digest table", "denormalize", or "thundering herd" in the context of Convex.

日本語の概要は準備中です。原文の説明を表示しています。

Weekly review partner that compounds productivity gains over time. Use when: (1) "weekly review", "what did I accomplish this week?", "compound my gains", (2) End of work week to review progress, (3) "what patterns are emerging?", (4) "update the system map", (5) Feeling stuck and need to identify friction. Reads all system/ files to synthesize progress. Outputs to system/compounder/week-{date}.md. Feeds insights back to /audit.

日本語の概要は準備中です。原文の説明を表示しています。

Productivity analyst that maps your workflow, scores tasks, and builds a prioritized automation plan. Use when: (1) "audit my workflow", "what should I automate?", "productivity review", (2) Starting a new project and need to identify high-leverage tasks, (3) Feeling overwhelmed and need to prioritize, (4) After /compounder surfaces new friction or patterns. Reads compounder insights when available. Outputs to system/audit-report.md.

日本語の概要は準備中です。原文の説明を表示しています。

Weekly review partner that compounds productivity gains over time. Tracks velocity, logs friction, sets next-week targets, recognizes patterns across weeks, and feeds insights back to the audit for the next loop. Use when you want a weekly review, need to identify friction, want to see patterns, or want to update your system map. Part of the architect-system loop. Outputs to system/compounder/week-{date}.md.

日本語の概要は準備中です。原文の説明を表示しています。

No-code automation democratizes workflow building. Zapier and Make (formerly Integromat) let non-developers automate business processes without writing code. But no-code doesn't mean no-complexity - these platforms have their own patterns, pitfalls, and breaking points. This skill covers when to use which platform, how to build reliable automations, and when to graduate to code-based solutions. Key insight: Zapier optimizes for simplicity and integrations (7000+ apps), Make optimizes for power

日本語の概要は準備中です。原文の説明を表示しています。

Workflow automation is the infrastructure that makes AI agents reliable. Without durable execution, a network hiccup during a 10-step payment flow means lost money and angry customers. With it, workflows resume exactly where they left off. This skill covers the platforms (n8n, Temporal, Inngest) and patterns (sequential, parallel, orchestrator-worker) that turn brittle scripts into production-grade automation. Key insight: The platforms make different tradeoffs. n8n optimizes for accessibility

日本語の概要は準備中です。原文の説明を表示しています。

Voice agents represent the frontier of AI interaction - humans speaking naturally with AI systems. The challenge isn't just speech recognition and synthesis, it's achieving natural conversation flow with sub-800ms latency while handling interruptions, background noise, and emotional nuance. This skill covers two architectures: speech-to-speech (OpenAI Realtime API, lowest latency, most natural) and pipeline (STT→LLM→TTS, more control, easier to debug). Key insight: latency is the constraint. Hu

日本語の概要は準備中です。原文の説明を表示しています。