「orpo」の検索結果

Shape FriendlyBet product decisions, MVP scope, user stories, tournament flows, multi-sport expansion, pool mechanics, social competition loops, and feature prioritization. Use when planning what FriendlyBet should build, cut, sequence, or validate, especially for new sports, prediction formats, pool flows, leaderboards, sharing, or product strategy.

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。

Shape FriendlyBet Pundit content, bilingual product copy, community voice, share-card copy, social moments, video briefs, Hebrew/English wording, and group-chat friendly communication. Use when a task touches copy, Pundit tone, social videos, share text, community loops, or public-facing FriendlyBet voice.

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。

Review FriendlyBet quality, regression risk, test plans, release readiness, version bump discipline, scoring/locking correctness, provider fallback behavior, and dirty-worktree safety. Use before shipping app code, scoring changes, migrations, sports integrations, copy updates, PWA cache changes, or any risky user-facing flow.

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。

Act as FriendlyBet's CEO and primary front door for every FriendlyBet request through automatic company preflight. Own end-to-end routing, planning, execution coordination, department orchestration, decision-rights enforcement, quality gates, board-style reporting, and escalation only for strategic, legal, brand, cost, risk, or taste decisions. Use by default in FriendlyBet threads, with full department planning for broad, ambiguous, strategic, cross-functional, or outcome-driven requests such as SEO, new features, multi-sport expansion, product direction, release execution, company operations, or agent coordination.

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。

Review or design FriendlyBet user experience, visual direction, mobile-first flows, premium sports interface, Hebrew/English copy fit, RTL behavior, accessibility, interaction patterns, and product UI polish. Use when a task touches screens, flows, layout, visual hierarchy, onboarding, prediction UX, share UX, or design quality.

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。

Plan or review FriendlyBet architecture, implementation, schema, data flows, scoring, provider integrations, static/PWA behavior, Supabase/RLS impact, migrations, automation, and technical sequencing. Use when a task asks how to build, refactor, generalize, integrate, test, or safely ship FriendlyBet code.

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。

Create or refresh FriendlyBet Story of the World Cup image assets and app overlays. Use when generating new 9:16 World Cup story artwork, replacing story-assets PNGs, writing image-generation prompts, checking real player shirt numbers, placing Hebrew/English story captions, or validating that story text does not cover player faces or get cropped in WhatsApp previews.

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。

Coordinate FriendlyBet company-level strategy, CEO handoff, department orchestration, HR/agent-quality escalation, operating-model decisions, multi-agent synthesis, roadmap tradeoffs, decision rights, quality gates, and updates to the virtual company system. Use when Eyal asks to involve the company, CEO, leadership, strategy, operations, several departments, or named executive agents such as Strategy Director, Chief of Staff, or Operating System Lead.

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。

Control FriendlyBet operating cost, free-tier viability, provider/API cost, GitHub Actions/Supabase/Vercel usage, documentation hygiene, company memory updates, playbook maintenance, and skill validation. Use when evaluating cost, operational burden, tooling, internal docs, company memory, or whether agent profiles should become full skills.

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。

Plan FriendlyBet ethical growth, SEO, README positioning, open-source contribution paths, self-hosting story, public trust, and discovery without ads, trackers, spam, or dark patterns. Use for repo-facing docs, growth strategy, launch positioning, SEO pages, contributor onboarding, or public messaging.

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。

Review FriendlyBet privacy, RLS security, auth, recovery codes, session storage, data minimization, public snapshots, share surfaces, legal/gambling wording, real-money risk, and compliance-sensitive product changes. Use when a task changes data access, user identity, public sharing, money-like language, security posture, or legal risk.

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。

Model sports rules, event formats, prediction types, scoring implications, lock rules, data requirements, and provider constraints for FriendlyBet. Use when adding or comparing sports such as football, basketball, American football, tennis, tournaments, leagues, playoffs, brackets, weekly picks, or World Cup-specific logic.

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。

Govern FriendlyBet agent values, professional standards, anti-hallucination behavior, truthfulness, risk reporting, AI resource discipline, feedback loops, skill improvement, performance reviews, decision-rights hygiene, and team quality. Use when creating, reviewing, promoting, correcting, or retiring agents; reducing hallucinations or mistakes; reviewing AI/tool usage; handling agent incidents; or aligning work with Eyal's philanthropic user-first approach.

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。

Connect to a running NeuroSkill instance and incorporate the user's real-time cognitive and emotional state (focus, relaxation, mood, cognitive load, drowsiness, heart rate, HRV, sleep staging, and 40+ derived EXG scores) into responses. Requires a BCI wearable (Muse 2/S or OpenBCI) and the NeuroSkill desktop app running locally.

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。

Expert en gouvernance d'entreprise (board management, compliance, DZ regulations, OHADA)

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。

Autonomously deep-scan entire codebase line-by-line, understand architecture and patterns, then systematically transform it to production-grade, corporate-level professional quality with optimizations

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。

Autonomously deep-scan entire codebase line-by-line, understand architecture and patterns, then systematically transform it to production-grade, corporate-level professional quality with optimizations

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。

Plain language directive. Cuts verbosity, corporate jargon, and overused AI vocabulary. Use when you want clear, direct, short writing. No fluff, no hedging, no unnecessary complexity.

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。

Execute an internal network penetration test simulating an insider threat or post-breach attacker to identify lateral movement paths, privilege escalation vectors, and sensitive data exposure within the corporate network.

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。

GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.

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。

Professional, brand-aligned design with structured grids, minimalist layouts, and consistent enterprise patterns.

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。

Read all suggested PR changes and apply them directly to the codebase. Use when you have PR review comments to address, suggested changes to implement, or feedback to incorporate into your code.

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。

Write content in David Dias's authentic voice. Use when Quill or any agent needs to create tweets, blog posts, emails, marketing copy, or any content that should sound like David wrote it. David's voice is personal and conversational, mixing professional insight with personal anecdotes. He shares failures openly, values community, and speaks directly to readers with honesty and warmth. Not corporate or overly polished.

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。

Expert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support

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。