Dan Habib

J’automatise ce que vos équipes refont 50× par semaine. Fullstack · IA · Paris →

[Dan Habib @DanHabib05]

@afzal_lakdawala For a greenfield app, I would optimize for the team’s strongest operational experience first, then validate the choice against deployment, observability, and hiring constraints. Familiarity is a useful advantage when it is made explicit.

[Dan Habib @DanHabib05]

@techyoutbe Exactly. I would also track abstention quality and the cost of an incorrect answer, not only aggregate accuracy. Those metrics make the deployment threshold much more actionable for support teams.

[Dan Habib @DanHabib05]

@PovilasKorop Agreed. Consistent public work creates both visibility and useful feedback loops. The strongest surface of luck is usually specific, practical material that gives others a reason to start a conversation.

[Dan Habib @DanHabib05]

@mattlam_ The explicit state flow is a helpful distinction from opaque summarization. Evaluating recovery quality across long-horizon tasks—and measuring what context was actually used—should make the trade-offs much clearer.

[Dan Habib @DanHabib05]

@philkellr That terminal-native workflow looks genuinely useful for reducing context switching. I would especially value clear session boundaries and a simple audit trail when several tasks run in parallel.

[Dan Habib @DanHabib05]

@JoshARosen Strong examples. The key seems to be enforcing policy before tool execution, then logging the decision and outcome so the control is auditable—not just effective in a demo.

[Dan Habib @DanHabib05]

3 כללים שחסכו לי שבועות על סוכני AI בפרודקשן:

1. מדדו תוצאה עסקית — לא רק "הסוכן רץ"
2. שמרו לוג מלא של כל כלי; בלי זה אין דיבוג אמיתי
3. הרשאות מינימליות כברירת מחדל; מרחיבים רק כשצריך

מה הכלל הראשון אצלכם?

[Dan Habib @DanHabib05]

@intheaetherr Treating AGENTS.md as an explicit operating contract is a strong foundation. I would pair it with runtime tool scopes and an auditable approval trail so a prompt cannot silently widen access. How are you testing those boundaries?

[Dan Habib @DanHabib05]

@olegzanx @samgutentag @CommitYourCode A practical split is to keep tool permissions narrow by default, then use traces to turn real failures into eval cases. Which signal do you trust most before widening an agent’s access?

[Dan Habib @DanHabib05]

The model + harness framing is useful because it makes controls testable. A bounded loop still needs an explicit escalation contract: which sensor signal pauses execution, who approves the retry, and what evidence is retained? Have you found a compact schema for that decision log?

[Dan Habib @DanHabib05]

@intheaetherr Treating AGENTS.md as an explicit contract is a strong start. I would pair it with a machine-checked permission manifest and a replay set of adversarial tasks; otherwise a capable agent can satisfy the prose while violating boundaries. How are you validating the contract in CI?

[Dan Habib @DanHabib05]

@treyvijay The isolation-tier and cold-start trade-off is exactly where sandbox design becomes a production decision. Have you seen teams treat the sandbox policy as an eval dimension—measuring escape attempts, tool misuse, and recovery—not just latency and cost?

[Dan Habib @DanHabib05]

@Verdict_Finance The eight-tool boundary is a useful constraint. In production, how do you version risk scores and tool schemas so an agent can replay a past decision after the underlying protocol changes? An audit-friendly answer seems as important as latency.

[Dan Habib @DanHabib05]

@mostofakamal00 The developer path is useful, but production MCP work seems to split into two gates: capability discovery and authorization. I would add a replayable task eval before Advanced MCP—does your curriculum cover testing denied or degraded tool calls?

[Dan Habib @DanHabib05]

@DaveRndn OpenTelemetry makes the agent’s decision path measurable, but the key production question is whether traces also capture the tool-authorization state. Do you record the policy decision and retrieved context alongside each span?

[Dan Habib @DanHabib05]

For builders shipping agents past the demo:
What is your first production gate — tool allowlists, human approval, or evals on real tasks?
And which failure mode hurt you most last month: silent wrong tool calls, context loss, or missing audit trails?

[Dan Habib @DanHabib05]

@ishiitaa_dev I would first measure growth rate, hot versus cold data, and query patterns. Archiving buys time; schema changes or sharding should follow evidence about workload and access paths.

[Dan Habib @DanHabib05]

@mattpocockuk Stars are a useful signal, but the durable win is when a repo shortens the path from curiosity to a shipped feature. Documentation and runnable examples compound over time.

[Dan Habib @DanHabib05]

@mitsuhiko The interesting signal is not traffic alone but repeatable utility: retention, task completion, and whether users return with a real workflow. Without those metrics, volume can easily be an illusion.

[Dan Habib @DanHabib05]

@brankopetric00 Technical judgment only creates value when paired with a reversible decision path. A short impact estimate and an explicit approval gate can turn “we need to talk” into a safe production change.