Results for “daytona”

Configure Jetson UPHY lane allocation on Orin/Thor custom carriers by editing ODMDATA tokens and dispatching per-controller kernel-DT overlay skills.

Generate production-ready Python code using the Dataverse SDK with error handling, retry logic, OData optimization, and logging.

Run static BSP checks and on-target smoke/regression tests on a flashed NVIDIA Jetson device to validate a customized BSP image.

Implements finite automata, regular expressions, parsers, and lexers for building text processors and pattern matchers.

Build monitoring dashboards that answer real operator questions for Grafana, SigNoz, and similar platforms, turning metrics into actionable operational views.

Use when a user asks to mine or update a private, evidence-backed work profile from local Claude Code, Codex, Copilot CLI, or OpenCode sessions.

Use when a user asks to mine or update a private, evidence-backed work profile from local Claude Code, Codex, Copilot CLI, or OpenCode sessions.

Provides a dashboard template for community/dating metrics with a navigation rail, ticker bar, headline KPIs, 30-day mutual-matches bar chart, and match-rate trend block.

Select alternative execution paths when a plan step fails — branch on outcomes instead of blind retry. Load when a structured plan step fails, an unexpected observation arrives, or the user asks what to try next after an error. Also triggers on "try another approach", "route around this failure", "replan on failure", "if X fails try Y", or outcome-based branching during multi-step work. Pairs with structured-planning and debug-and-fix. Does not replace root-cause debugging — adds plan-level path selection.

Race condition and TOCTOU testing for web apps. Use when testing one-time operations, concurrent HTTP abuse, rate-limit bypass, Turbo Intruder gates, HTTP/2 single-packet attacks, and CWE-362-style synchronization gaps.

Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.

SONA self-optimizing neural architecture with ReasoningBank trajectory learning, EWC++ anti-forgetting, and reinforcement learning feedback loops.

Write, review, debug, or test Python datetime, date, timedelta, timezone, and zoneinfo code, especially UTC conversion, DST gaps and folds, recurring local schedules, parsing, and interval boundaries.

为 Grafana、SigNoz 等平台构建能够回答实际运维人员问题的监控仪表板。适用于将指标转化为可用的仪表板，而非华而不实的展示板。

Selects Jetson-compatible containers, vLLM runtime images, and Jetson AI Lab PyPI indexes based on Orin SM 8.7 vs Thor SM 11.0 and JetPack version.

Generate production-ready Python code for Dataverse SDK with advanced patterns including error handling, batch operations, OData optimization, and Pandas integration.

Applies four disciplined mindsets—audit, debt, help, review—to cut complexity, track deferrals, surface reference, and catch over-engineering in codebases.

Generate aerial drone-perspective footage with sweeping bird's-eye views, orbit shots, and flyover sequences for landscapes, architecture, and events.

Design and review native DTOs in WordPress plugins without better-data, covering immutable data carriers, explicit hydration, strict coercion, WP_Error validation, sensitive-field handling, and layer boundaries.

Detects DNS tunneling by computing Shannon entropy of DNS query names, analyzing query length distributions, inspecting TXT record payloads, and identifying high subdomain cardinality using scapy for packet capture analysis.

琦玉（少年漫）认知与表达框架（压缩蒸馏）：无敌无聊、一拳解构热血、超市券日常 触发：一拳超人 等。虚构

Turn a new rep's start date into a ramp that ends in quota, not confusion. Builds a 30/60/90 onboarding plan with milestones at each gate, a certification bar they have to clear before they carry full load, and the manager touchpoints that catch a slow ramp early. Built for B2B sales managers and enablement, customizable to your role and motion. Trigger on "build an onboarding plan", "30/60/90 for a new AE", "how do I ramp this rep", "certification checklist", "onboarding milestones", or any new-hire ramp.

Volume-based PUMP token reward system with day-indexed epochs, pro-rata distribution, accumulator tracking, and cross-program claiming on Solana.

Post-execution skill crystallization and learning engine updates for WinDAGs. Runs after successful execution to update Thompson sampling parameters, track method quality, detect monster-barring, log near-miss events, and signal Kuhnian crises. Activate on "curator", "learning update", "skill crystallization", "Thompson sampling", "monster-barring", "near-miss", "Kuhnian crisis", "post-execution learning". NOT for pre-execution risk scanning (use windags-premortem), retrospective analysis (use windags-looking-back), or DAG construction (use windags-architect).

Create, name, scope, assign, and close GitHub milestones. Use when adding a milestone, deciding whether work belongs in one, or naming the next one. One axis per project (release / date / theme); never as labels.

Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.