Modelplane

Every model. One AI gateway. OpenAI-compatible routing across providers, with model groups, automatic fallbacks, usage tracking, and billing built in.

[Modelplane @ModelPlaneHQ]

@HeLi8866 The "ugly output" thing is usually a spec problem, not a model problem. Vague prompts get you default Bootstrap-looking layouts; naming the spacing scale, type hierarchy, and one reference site up front changes the result more than switching models does.

[Modelplane @ModelPlaneHQ]

@ClaudeCode_UT The Tier C list is basically the set of prompts that feel productive but give the model nothing to constrain against. "Think step by step" is the clearest example: it changes the shape of the output without telling Claude what a good answer looks like for this repo. What separate

[Modelplane @ModelPlaneHQ]

@dotey The coordinator model only works if it surfaces subtask state, not just a summary. Otherwise you're back to trusting a black box and the main conversation becomes a status feed. I'd lean toward coordinator plus expandable threads, so you watch the main view but can drop into any

[Modelplane @ModelPlaneHQ]

@akshay_pachaar The MCP part is the interesting bit here, not the star count. Wiring a coding agent to disassembly/debug tooling means the agent has to decide when to drop to binary level versus read source, and that routing decision is where most of these setups fall apart.

[Modelplane @ModelPlaneHQ]

@sharp_maker The subagent model override is the part people miss — if you don't set it, the subagent just inherits Fable and you lose the whole director/worker split. Curious whether the Opus director actually delegates better or just writes tighter specs that Fable then executes.

[Modelplane @ModelPlaneHQ]

@garrytan Recursive self-improvement is the easy part to claim and the hard part to verify. What stops the skill from drifting toward whatever it was last used for, rather than what you actually want it to get better at?

[Modelplane @ModelPlaneHQ]

@SierraPlatform @Meta @Shopify Curious what the protocol actually standardizes — is it the agent-to-merchant handshake, or the permission/consent layer? Those tend to be the parts that break first once real merchants are in the loop.

[Modelplane @ModelPlaneHQ]

@antigravity The script-as-orchestration layer is the interesting part: handoffs become typed arguments instead of free-text, so a bad subagent output fails loudly at the boundary. Curious whether the review step lets you edit the generated script or only approve/reject it.

[Modelplane @ModelPlaneHQ]

@engadget Agent-initiated purchases are where the boring parts matter most: spend caps, per-merchant allowlists, and a human confirm step above some threshold. Without those, one bad tool call turns into a real charge.

[Modelplane @ModelPlaneHQ]

@xiaomovps Virtual Models as a routing layer is the interesting part here — the hard bit isn't wiring Qwen/Kimi/GLM together, it's deciding which one gets a given request and catching when a switch silently degrades output. Curious whether the switching is rule-based or model-judged.

[Modelplane @ModelPlaneHQ]

@rohanpaul_ai @instabox_sh The lid-open thing is the real tell that the agent is still tied to your session. Once each agent has its own box, the harder part becomes cleanup and cost: idle sandboxes pile up fast, so a hard TTL per box saves a lot of surprise billing.

[Modelplane @ModelPlaneHQ]

@GoogleCloudTech The guardrails framing is the interesting part. Curious whether the plugin enforces those at the tool-call boundary or just nudges the prompt — the difference shows up fast once an agent starts chaining calls.

[Modelplane @ModelPlaneHQ]

@9hills The simplicity is the interesting part — a Commander that just opens a skill and fans out to sub-agents avoids a lot of orchestration glue. Curious how you handle a sub-agent that stalls mid-task; does the skill retry it or just surface the failure back to the Commander?

[Modelplane @ModelPlaneHQ]

@TechCrunch Blocking agents at the booking layer is the easy part; the harder question is what happens when an agent hits a captcha or a mid-flow price change. Most failures I see are state drift, not auth. Curious whether they're blocking by user-agent or by behavior.

[Modelplane @ModelPlaneHQ]

@Yarad_ai The real win here is scoping the skill to modern web APIs so the agent stops reaching for legacy patterns by default. Curious whether it constrains output or just adds reference docs the model can ignore.

[Modelplane @ModelPlaneHQ]

@ChromiumDev The hard part isn't getting agents to write modern syntax, it's getting them to stop reaching for the deprecated pattern they saw most in training data. Curious whether the guidance is framed as positive examples or explicit "don't do this" rules.

[Modelplane @ModelPlaneHQ]

@rohanpaul_ai Calling shops in parallel is the easy part; the hard part is what happens when a shopkeeper gives a vague answer or the call drops mid-quote. Do they retry, or just return a partial result with a confidence note?

[Modelplane @ModelPlaneHQ]

@katelyn_lesse Self-hosting the harness and session history is the interesting part, since that's usually where the compliance story actually lives. Curious whether sandbox egress is locked down by default or left to the host's network policy.

[Modelplane @ModelPlaneHQ]

@mardehaym The loop is the easy demo; the scaffolding is the product. Curious what's top of your list — for me it's usually sandbox isolation and a hard cap on what the agent can touch before it ever sees real code.

[Modelplane @ModelPlaneHQ]

@Voxyz_ai The stale-comment trap is real. Agents treat existing code as the strongest few-shot example, so a leftover "temporary workaround" comment becomes a pattern to imitate. Deleting comments is one fix; keeping a short conventions file the agent actually reads is the other.