bhekanik

Really like the strict network/domain separation + Lua-at-the-edges approach here. In game servers, once simulation logic and protocol plumbing get tangled, every feature turns into surgery. Keeping a stable C# core and pushing gameplay iteration into scripts seems like the right tradeoff.

Curious if you’ve seen any hotspots at the C#↔Lua boundary under load (e.g. per-tick callbacks on many entities), and whether you batch/cap script invocations per tick to keep tail latency predictable.

Feels like we’re relearning a very old lesson: if untrusted text can influence a privileged runtime, you need hard isolation boundaries, not policy in prompts. For agent-based workflows I’d default to (1) no network egress unless required, (2) ephemeral runners with no shared cache, (3) narrowly scoped tokens, and (4) a mandatory human approval step before any write action. Slightly slower, but much cheaper than incident response.

Nice work shipping this.

I’ve found the hardest part with agent frameworks isn’t model plumbing, it’s operational boundaries: how you isolate tools, enforce time/budget limits, and recover from partial failures when an agent call chain fans out.

BEAM’s supervision model feels like a genuinely strong fit for that, especially if each tool execution can be treated as a supervised unit with clear restart/escalation semantics. Curious whether you’ve seen teams default to many small specialized agents vs fewer general agents with stricter policies.

I think the framing as “lying” is emotionally accurate for users but technically misleading for builders. Most failures I see in production aren’t intentional deception, they’re confidence calibration failures: the model sounds certain when it should sound tentative.

What helped us most was treating outputs like untrusted input: retrieval + citations for factual claims, strict tool permissions, and an explicit “I don’t know” path that’s rewarded instead of punished. That doesn’t make LLMs truthful, but it does make them a lot less brittle.

So to me the key question isn’t “are models liars?” but “what product and engineering constraints make wrong answers cheap and detectable?”

Strongly agree with the patterns list, but I think the order matters: observability and evals should come before sophistication.

Teams jump straight to multi-agent setups because it feels "advanced," then spend weeks debugging ghosts. A boring single-agent flow with strict tool contracts, replayable traces, and a small regression suite beats a clever architecture you can't reason about.

My rule of thumb now: if I can't explain why an agent made a decision from logs alone, it's not production-ready yet.

This is a solid framing. In my experience the nasty regressions are rarely a bad single response; they are state drift over 6-12 turns (verification skipped, tool called in the wrong order, recovery path never triggered).

One thing that's helped us is tagging each test with an explicit risk class (safety/compliance, business logic, UX) and tracking those buckets over time instead of relying on one pass/fail number. Release decisions get much less hand-wavy when one category starts creeping.

Session-level eval plus risk-bucket trends feels like the right combo for teams shipping agents weekly.

As a dev, I think the core issue isn’t whether one indicator LED can be bypassed — motivated people can bypass almost any client-side control. The trust boundary is policy + defaults. If enabling “AI features” implicitly authorizes broad retention/review, users won’t understand the tradeoff until after the fact.

A better pattern would be tiered modes with explicit UX: local-only capture, cloud processing without retention, and opt-in retention/training with visible status. If the product can’t technically support that separation today, that limitation should be stated plainly in setup, not buried in policy docs.

I built this after watching OpenClaw incidents pile up across WIRED, CyberNews, Reddit, and HN itself. Stories are ranked by a composite Horror Score that factors in community votes, authenticity signals (receipts required), and evidence quality. Heavily-documented stories rise; flagged-as-fake stories sink. Built with Next.js + Convex (real-time backend) + shadcn/ui. Dark mode by default. Source: https://github.com/bhekanik/openclaw-horror-leaderboard

Great discussion! As someone who works with AI coding agents daily, my take is that the "best" language really depends on what the agent is building. Go's simplicity and predictability are huge for general-purpose agents, but I've found TypeScript shines for agents that live in the web ecosystem - interacting with APIs, browser automation, etc. The ecosystem alignment matters a lot. Python will always have a stranglehold on data/ML workloads simply because that's where the libraries are. The key insight might be: pick the language that matches your agent's domain, not just what the LLM generates best.

The skill-based approach has been a game changer for me. Instead of wrestling with MCP server configs, I just write a small markdown file describing the CLI tool and its common use cases. The agent loads it on demand when it detects the tool is relevant. Much simpler than maintaining a running process, and the documentation lives right next to my code rather than in some separate server config. The composability argument is what really sold me - piping through jq, chaining commands, redirecting to files... that's stuff I do daily and never have to think about.

Great project! The self-learning memory approach is smart - I've found that persistent context across agent runs is what separates useful automation from novelty. The shared vs personal memory distinction sounds similar to how humans work: individual notes that compound into team knowledge. The evolution approach you describe (start small, then expand) really is the pragmatic way to adopt these tools. The "it won't go rogue" jokes are funny but the real risk I've seen is more mundane - agents quietly doing the wrong thing confidently. Memory and reflection loops like you're building help with that too.

The worktree isolation is clever, but I'm curious about the security model when you're running multiple agents in parallel with shell access.

In my experience running CLI-based agents, the biggest risk isn't the agent "going rogue" in the sci-fi sense — it's context window drift during long sessions. Agent compacts its history, loses track of which worktree it's in, and starts editing files in the wrong branch. Or worse, running tests against the wrong database.

Git worktrees help with file isolation, but they share the same .git directory and can still interfere with each other during concurrent operations (rebases, reflog contention). Have you run into this with 5+ parallel agents?

The native CLI approach is smart for staying current with provider features, but it does mean you're trusting each provider's sandboxing. Some are better than others. Claude Code's allowlists are reasonably paranoid; others less so.

Nice to see someone building tooling for this workflow rather than trying to replace the terminal entirely.

As someone who uses AI coding agents daily, the thing that still surprises me is how much faster I can iterate on the "problem space" vs the "solution space." I can try 5 different approaches to a problem in the time it used to take to implement one. The code itself is cheap now, but the expensive part—figuring out what to build—hasn't changed. If anything, being able to quickly test ideas makes it more important to be clear on what you're actually trying to solve.