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![The image is a screenshot of a social media post, likely from X (formerly Twitter), featuring a user's profile, a long text post discussing 'evals' in the context of AI and coding companies, and an embedded preview of a video or web page with a transcript.. Text: swyx
@swyx
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Claude Code: no evals
[well known code agent company]: no evals
[well known code agent company 2]: kinda halfassed evals
[leading vibe coding company]: no evals
[ceo of company selling you evals]: mmmmm yess all my top customers do evals, you should do evals
[vc's in love with ceo of evals company]: mmmmm yess all my top founders do evals, must do evals
(NOTE: i -do- also think that evals are impt, but the eval pilled ai engineers have also noticed that it is not a strict requirement for success and, at least for 0-to-1 stage, may even be anticorrelated, think thru why)
(4) The future of agentic co...
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evals
- Yeah, so the best eval in some sense is
the one that most looks like real life.
And in that case, just using it gives you the
best result.
- We tried really hard, when building Claude
Code, to build a product evals.
- Yeah](https://images.aitinkerers.org/cdn-cgi/image/width=700,fit=scale-down,format=auto,quality=68/blog_images/swyx-png-6O-h.png)
Voice agents are hard because most use cases are long, multi-turn conversations and people don’t talk like they write, so LLM performance degrades over turns. Use context engineering + state machines (system prompt, conversation summary, limited tool list, next states). For confidence: do earnest manual testing, then lightweight/repeatable evals. I benchmarked a 30-turn voice scenario with ~75k-token context: every model had errors.
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"POST-TRAINING"
PALLADIUM
ELENIOM
BANDICAM
GARDENWARE
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ENIGMA
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Mianny Hotel
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AL PRUUCE MAGNA COREM ABDUILE BERLLINE ME CEULANTEPIC AN
[numerous other small, unreadable labels and names]](https://images.aitinkerers.org/cdn-cgi/image/width=700,fit=cover,format=auto,quality=68,height=475/blog_images/post-training-screen-png-7AO1.png)
This week’s top demos feature Swift-native agents, micro:bit MPC, and Infinite Brainrot—an inviting snapshot for the community to explore these projects.
Local AI brings browser agents and ultra-low-latency meeting assist, delivering fast, client-side AI support directly in meetings.
![A screenshot of a dark-themed integrated development environment (IDE) displaying Rust programming code, with line numbers and syntax highlighting. On the right, a communication thread is visible, discussing the code's indentation levels and an embedded image of code.. Text: alex
1 month ago in # random - image.png
executor.rs
task_attempts.rs
claude.rs
amp.rs M
db.sqlite
backend > ~/bloop/vibe-kanban/backend/src/executor.rs tor for AmpExecutor > normalize_logs > [@] proce
26 impl Executor for AmpExecutor {
91 fn normalize_logs(
130 let processed = if let Some(msg_type) = json.get("type").and_then(|t|
entries.push(
timestamp: None,
entry_type:
NormalizedE
tool_nam
.to_
action_t
}
Thread
alex Jul 10th at 3:34 PM
amp.rs has 20 levels of indentation
image.png
1 reply
Louis Knight-Webb Jul 10th at 3:49 PM
Probably the most AI generated part of the codebase
Reply...
Also send to # random](https://images.aitinkerers.org/cdn-cgi/image/width=700,fit=scale-down,format=auto,quality=68/blog_images/image1-png-bnme.png)
Vibe Kanban showed how to ship a real CLI-built app: we capped PRs to <400 LOC and <10 files with a dedicated planning step (affected files, schema changes, test plan), used ts-rs to enforce shared Rust↔TS types via CI regeneration, ran agents in fresh per-session SQLite dev environments, and enforced separation of concerns with Rust crate boundaries (API can’t import SQL/git).
Claude Code is an agentic CLI that loops through task → subtasks → execution → reflection, producing files, text, and bash commands; to get strong results, use Anthropic’s Explore/Plan/Execute/Commit, keep context clean with `CLAUDE.md` (root + parents, plus subfolders), and reuse sessions via `/resume` and double escape (Esc+Esc); power up with custom slash commands, subagents, and hooks, plus MCP servers and GitHub integration via `install-github-app` and `@claude`.
Practical AI Builds returns on August 11 with hands-on formats covering Scheduling Agents, MoE Assembly, Virtual Try-On, and more—delivering actionable techniques, real-world demos, and community-driven insights for builders and researchers.
We built Lovable for AI Agents because agent building gets stuck on framework choices, tool/auth integration, model selection, and orchestration—so we skip that and generate a working agent in under 10 minutes. Using Composio’s managed tools (execution + authentication for 500+ apps), the app analyzes your use case, selects tools (e.g., Gmail Fetch Emails + Notion Create Page), creates connections via `/api/create-connection`, then dynamically generates a custom frontend + Next.js backend via `/api/generate-agent` for instant deployment.
Cut latency, ditch the cloud—our hottest AI tinkerer demos on July 28.
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