3.6 KiB
Screenshot Dump Triage — Visual Inspiration & Research Leads
Date: March 24, 2026 Source: Issue #1275 — "Screenshot dump for triage #1" Analyst: Claude (Sonnet 4.6)
Screenshots Ingested
| File | Subject | Action |
|---|---|---|
| IMG_6187.jpeg | AirLLM / Apple Silicon local LLM requirements | → Issue #1284 |
| IMG_6125.jpeg | vLLM backend for agentic workloads | → Issue #1281 |
| IMG_6124.jpeg | DeerFlow autonomous research pipeline | → Issue #1283 |
| IMG_6123.jpeg | "Vibe Coder vs Normal Developer" meme | → Issue #1285 |
| IMG_6410.jpeg | SearXNG + Crawl4AI self-hosted search MCP | → Issue #1282 |
Tickets Created
#1281 — feat: add vLLM as alternative inference backend
Source: IMG_6125 (vLLM for agentic workloads)
vLLM's continuous batching makes it 3–10x more throughput-efficient than Ollama for multi-agent
request patterns. Implement VllmBackend in infrastructure/llm_router/ as a selectable
backend (TIMMY_LLM_BACKEND=vllm) with graceful fallback to Ollama.
Priority: Medium — impactful for research pipeline performance once #972 is in use
#1282 — feat: integrate SearXNG + Crawl4AI as self-hosted search backend
Source: IMG_6410 (luxiaolei/searxng-crawl4ai-mcp)
Self-hosted search via SearXNG + Crawl4AI removes the hard dependency on paid search APIs
(Brave, Tavily). Add both as Docker Compose services, implement web_search() and
scrape_url() tools in timmy/tools/, and register them with the research agent.
Priority: High — unblocks fully local/private operation of research agents
#1283 — research: evaluate DeerFlow as autonomous research orchestration layer
Source: IMG_6124 (deer-flow Docker setup)
DeerFlow is ByteDance's open-source autonomous research pipeline framework. Before investing further in Timmy's custom orchestrator (#972), evaluate whether DeerFlow's architecture offers integration value or design patterns worth borrowing.
Priority: Medium — research first, implementation follows if go/no-go is positive
#1284 — chore: document and validate AirLLM Apple Silicon requirements
Source: IMG_6187 (Mac-compatible LLM setup)
AirLLM graceful degradation is already implemented but undocumented. Add System Requirements
to README (M1/M2/M3/M4, 16 GB RAM min, 15 GB disk) and document TIMMY_LLM_BACKEND in
.env.example.
Priority: Low — documentation only, no code risk
#1285 — chore: enforce "Normal Developer" discipline — tighten quality gates
Source: IMG_6123 (Vibe Coder vs Normal Developer meme)
Tighten the existing mypy/bandit/coverage gates: fix all mypy errors, raise coverage from 73%
to 80%, add a documented pre-push hook, and run vulture for dead code. The infrastructure
exists — it just needs enforcing.
Priority: Medium — technical debt prevention, pairs well with any green-field feature work
Patterns Observed Across Screenshots
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Local-first is the north star. All five images reinforce the same theme: private, self-hosted, runs on your hardware. vLLM, SearXNG, AirLLM, DeerFlow — none require cloud. Timmy is already aligned with this direction; these are tactical additions.
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Agentic performance bottlenecks are real. Two of five images (vLLM, DeerFlow) focus specifically on throughput and reliability for multi-agent loops. As the research pipeline matures, inference speed and search reliability will become the main constraints.
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Discipline compounds. The meme is a reminder that the quality gates we have (tox, mypy, bandit, coverage) only pay off if they are enforced without exceptions.