Results for “memory”

NVIDIA CUDA GPU 並列コンピューティングリファレンス。 CUDA C++ / CUDA Python, kernel, nvcc, Unified Memory, CUDA Graphs, Cooperative Groups, Green Contexts, Driver API, マルチ GPU。 PTX ISA 命令セット, state space, MMA 命令。 Blackwell チューニング, Streaming Multiprocessor, NVLink, Locality Domains, Compute Fabric Transport。 CUTLASS / CuTe DSL / CuTe C++ GEMM, Tensor Core, tcgen05, Task Scheduling, Operator API, nvMatmulHeuristics, nsight-compute, compute-sanitizer。

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Hermes Agent (Nous Research の AI CLI エージェント)。インストール、 CLI コマンド (chat, model, gateway, config, cron, webhook, mcp, skills, profile, hermes pm)、 Configuration (Terminal Backends, Memory, TTS / STT, Auxiliary Models, Compression, Display, Local Models llama.cpp, Egress / Secrets)、 Features (Tools, Skills, Hooks, Delegation, Kanban, Cron, Voice Mode, Personality, Context Files)、 Messaging Gateway (Platform Support, Bot Mode, Connectors)、Security、Architecture、 Hermes Desktop、Plugin Catalog、Profiles、Developer Guide、Nous Portal (Tool Gateway)。

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Claude Code (code.claude.com) の CLI 本体リファレンス。 CLAUDE.md memory, settings.json (All settings, settings precedence, managed-settings.json), env-vars, model config, keybindings, statusline, terminal config, cli-reference (-p / --continue / --resume), sessions, checkpointing, worktrees, remote control, channels, deep links, troubleshooting。

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Claude Managed Agents (platform.claude.com) リファレンス。Agents / Sessions / Environments API, SSE event streaming, event deltas, session threads, session budgets, multiagent orchestration (subagents, dynamic workflows, workflow runs, advisor), deployments (cron), dreams, vaults, memory stores, self-hosted sandboxes, webhooks, web search / fetch ドメイン制限, secure deployment, permission policies, Agent Skills, MCP connector, user profiles。

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Claude API (platform.claude.com) の tool use / Agent Skills / MCP。 tool definition (JSON Schema), tool_use / tool_result, server tools（code execution, web search, web fetch, memory tool, tool search）, client toolset（computer use, browser use）, Agent Skills (SKILL.md), Skills API GA (/v1/skills), MCP connector, mcp_servers, mcp_tool_listing, MCP tunnels, Tunnels API。

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Apple Silicon GPU コンピュート／数値計算リファレンス。MSL (address space, atomic, SIMD-group, TensorOps, cooperative_tensor, matmul2d), threadgroup sizing, argument buffer, indirect command buffer, unified memory, GPU counters, MTLTensor, MTL4MachineLearningCommandEncoder, MPS / MPSGraph / MPSMatrix / MPSNDArray / MPSNNGraph, MLX (mx.array, lazy evaluation, grad / vmap, mx.compile, mx.fft, export_function, mlx.nn, distributed)。

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Help job seekers use Otameshi Tenshoku to prepare profiles and applications, clarify trial conditions, plan learning visits or paid trial work, and reflect on the experience. Use when a candidate wants to apply for, prepare for, negotiate the scope of, or review an otameshi work experience; do not use to predict whether they will be hired or to judge their aptitude, to state fees, contract terms, or service conditions from memory, or to submit an application, update a profile, or send a message on their behalf. Employer-side requests belong to the employer skills (trial-work-design, trial-listing-draft, applicant-reply and others).

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Prepare the internal case for hosting an Otameshi Tenshoku trial: total what it costs the company with candidate pay, staff hours and platform or incidental fees kept apart, compare it with the budget, state what remains if no hire follows, and lay out the risks, stop conditions, decision owners and deadlines. Use when a hiring manager needs approval or budget from a decision maker, a recruiter has to explain the plan to the team that will host, or a small-business owner wants to see the full cost including their own time before posting; do not use to design the work (trial-work-design), to write the listing (trial-listing-draft), to predict whether a hire will follow, to state platform fees, agency fees or market rates from memory, or to submit the request or post the listing on the user's behalf.

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Draft or review an employer's Otameshi Tenshoku listing so a candidate can decide whether to apply: check item by item that the work, deliverable, out-of-scope tasks, required experience, support, hours, schedule, location, compensation, contract form and payment timing are stated, find open-ended, unpaid-company-work, guarantee, personal-attribute and unverifiable wording, and confirm that the hours, weekly availability, period, rate and budget hold together. Use when a hiring manager, recruiter or small-business owner wants a listing written from a plan, wants an existing draft or published listing reviewed before or after posting, or wants to know why applications are not coming; do not use to design the work itself (trial-work-design), to reply to applicants (applicant-reply), to state market rates, legal, tax or service conditions from memory, or to post or update the listing on the user's behalf.

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Design what an employer will have a candidate do during an Otameshi Tenshoku trial: turn a business problem into a bounded paid assignment or an unpaid learning visit, with scope, completion criteria, candidate hours, company staff hours, duration, compensation and change conditions, and check the planned cost against the budget. Use when a hiring manager, recruiter or small-business owner wants to decide what work to offer, how much time it takes, what it costs, or how to adjust a plan when materials are late or the scope grows; do not use to write the public listing (trial-listing-draft), to reply to applicants (applicant-reply), to state market rates, legal, tax or service conditions from memory, or to post, contact or contract on the user's behalf.

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Inventory a person's work history as facts before any document is written, listing each period of employment and what they actually did, splitting every experience into situation, their own actions, outcome, and stated role, and recording what each claim rests on (public record, their own records, someone who was there, or memory alone). Use when a user is starting a job search, needs material for a rirekisho, shokumu-keirekisho, or entry sheet, or wants to find which periods of their history they have never written down; do not use to judge their strengths, aptitude, or market value, to name a job they are suited for, or to write finished application copy.

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Work on lyrics — the web fallback (LRCLIB, Musixmatch, NetEase, Genius), the in-memory cache, verification, and embedding lyrics into audio file tags. Use this whenever a task touches `services/lyrics.py`, `services/tags.py`, the `/lyrics.json` route, or `scripts/embed_lyrics.py` / `embed_lyrics_cron.sh` — adding or fixing a provider, changing caching/verification, or the batch tagger. Covers the provider contract, the synced-vs-plain rule, the read-only cache, and why writes only ever store plain text.

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Assembles a genome de novo from Illumina short reads with SPAdes (isolate/careful/sc/meta/plasmid/rna modes), MEGAHIT (low-memory, huge datasets), Unicycler (bacterial finishing/hybrid), MaSuRCA (large hybrid), ABySS (Bloom-filter), and Platanus (heterozygous diploids), using multi-k de Bruijn graphs. Covers the repeat-resolution limit, why N50 plateaus at the genome not the depth, GenomeScope2 k-mer profiling first, the heterozygosity/haplotig trap, error-correction erasing rare alleles, GC dropout, and NG50/auN/BUSCO reporting. Use when assembling a bacterial isolate, fungal, small-eukaryotic, single-cell, or metagenome genome from Illumina reads, or when deciding whether short reads can even produce the assembly being asked for.

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Align preprocessed CLIP-seq reads (eCLIP, iCLIP, iCLIP2, PAR-CLIP) to genome with STAR or bowtie2 using crosslink-preserving parameters, choosing between unique-mapper-only and multi-mapper-aware alignment for repeat-binding RBPs, deciding STAR vs HISAT2 memory trade-offs, and applying ENCODE-compatible filters. Use when turning preprocessed CLIP FASTQ into a deduplicated, MAPQ-filtered BAM ready for peak calling or crosslink-site detection.

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Provides integration patterns for LangChain4j with Spring Boot. Configures AI model beans, sets up chat memory with Spring context, integrates RAG pipelines with Spring Data, and handles auto-configuration, dependency injection, and Spring ecosystem integration. Use when embedding LangChain4j into Spring Boot applications, building Java LLM applications with @Bean configuration, or setting up Spring AI patterns.

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Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration.

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Tag a thesis's implicit macro bets — the macro variables its bull case is secretly levered to (AI capex, rates, FX, China decoupling, memory/HBM cycle, energy, regulation) with direction and magnitude. Use when user says "macro exposure", "what macro is [TICKER] betting on", "macro bets", or "rate exposure for [TICKER]".

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Expert guidance for fast fine-tuning with Unsloth - 2-5x faster training, 50-80% less memory, LoRA/QLoRA optimization

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Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.

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Serves LLMs with high throughput using vLLM's PagedAttention and continuous batching. Use when deploying production LLM APIs, optimizing inference latency/throughput, or serving models with limited GPU memory. Supports OpenAI-compatible endpoints, quantization (GPTQ/AWQ/FP8), and tensor parallelism.

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Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.

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Autonomous rule adherence checker. Scans the codebase for rule violations, fixes the highest-impact ones in an isolated worktree, runs full validation, creates a PR. Uses memory to track progress across runs.

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Automated coordination, formatting, and learning from Claude Code operations using intelligent hooks with MCP integration. Includes pre/post task hooks, session management, Git integration, memory coordination, and neural pattern training for enhanced development workflows.

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Achieve aggressive v3 performance targets: 2.49x-7.47x Flash Attention speedup, 150x-12,500x search improvements, 50-75% memory reduction. Comprehensive benchmarking and optimization suite.

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