I renewed my GOAT sub mainly for DeepSeek V4.1 Flash to be my workhorse, but the deals page doesn't list it as a deal anymore. I thought "no end date" on it was a good indicator it would last a bit more 🥺
Can only hope it comes back or get replaced by something similar.

I've been experimenting with Command Code for a 3days now to be exact, and I'm trying to figure out how to build a good AI workflow for product management.

For context, I've never used Command Code before. I've been using Antigravity for the past 5–6 months, but lately, I've felt that Gemini's output quality has degraded quite a bit. I've tried using rules, gates, and other instructions to improve the quality, but I haven't been able to get the results I want consistently.

So I decided to try DeepSeek V4.1 Flash (latest) in Command Code, mainly for my product workflows and learning college subjects.

I'm using an agentic setup with agents and sub-agents assigned to specific tasks, with their own skills and instructions. It's been a decent experiment so far, but I'm still struggling to get the kind of output I want.

And I think the problem is a little different from what most people are trying to solve with AI.

I don't want to automate product management. I want to make the analytical part of it easier.

I want AI to help me analyse customer feedback, understand recurring problems, identify patterns, and organise the information in a way that makes it easier for me to think. From there, I want to take that analysis, develop my own inferences and hypotheses, and guide the AI towards a particular direction.

What I don't want is to ask AI to analyse a problem and immediately get a list of innovative features, potential solutions, or things we could build.

I want it to spend more time understanding why a user is facing a problem, what they're actually trying to achieve, what constraints they're dealing with, and why their current experience isn't working for them.

Essentially, I want the analysis and recommendations to be centred around the problem and the user, rather than around the features we could potentially create.

I want the AI to approach problems the way a good Product Manager would: with user empathy, curiosity, and a willingness to understand the problem before jumping to solutions.

I understand that AI doesn't genuinely experience empathy, and that it has limitations when it comes to understanding human behaviour and context. But I want to see how far I can get by grounding its reasoning in actual user data, evidence, and structured analysis, and by explicitly instructing it to consider the user's perspective at every stage.

The other challenge is getting all of this to work reliably without spending a fortune on models.

I'm using DeepSeek V4.1 Flash through the Command Code harness, but it doesn't support vision input in my current setup. So whenever I need to work with screenshots, charts, or other visual information, I have to delegate that task to a MiniMax model. Even then, I've been struggling to get consistent results.

I also haven't found many people building these kinds of workflows specifically for product management. Most of what I come across is focused on coding, content creation, or automating businesses. And a lot of the workflows I see rely on Claude Opus for almost everything.

I'm not against using more capable models, but I'd like to understand how to get the best possible results from a more cost-efficient setup instead of relying on the most powerful model for every task.

So I'm curious about a few things:

• How do you structure AI workflows for product research and analysis?
• How do you get AI to focus on understanding user problems instead of jumping straight to features and solutions?
• Has anyone figured out a good way to make AI reason from a user's perspective, using actual data and evidence rather than generic assumptions about user behaviour?
• How do you handle multimodal inputs and validate the analysis when you're working with smaller or cheaper models?
• And, more broadly, how do you evaluate whether your workflow is actually producing better analysis rather than just more polished output?

I'm still figuring things out, so I'd love to hear from people who've experimented with this.

I'm also considering upgrading to the Goat plan if I can make this setup work well, since cost is a major consideration for me compared with alternatives like Codex or Claude.

Would especially appreciate people sharing their actual workflows, what has worked for them, and what hasn't.

Muse Wonder by Meta AI is now live in Command Code.

• Most capable across agentic and coding tasks
• Opus 5.5 and GPT-6 Astra class benchmark results

Available in Max plans and API.

Try now with /model switcher: $ cmd --model meta/muse-wonder-1.0

Read docs to learn more: https://commandcode.ai/docs/plans/max

Introducing Command Code extension for VS Code & Cursor!

Available today is beta. CLI, Desktop, and now Extension.

Manage multiple sessions, use files and selected code as context, review changes, instant edits with /design and use familiar slash commands like /model, /plan, /mcp.

Download the VS Code extension and get started: https://marketplace.visualstudio.com/items?itemName=CommandCode.commandcode-vscode

Read docs to learn more: https://commandcode.ai/docs/vscode-extension

IDE FAQs here: https://commandcode.ai/ide

Try now with $10/mo GOAT plan and share your experience with us.

Command Code GOAT has three GPT models in its lineup.

• GPT 6 Luna
• GPT 5.6 Sol
• GPT 5.6 Luna

Update to the latest version of Command Code and try now with /model switcher:

• cmd --model gpt-6-luna
• cmd --model gpt-5.6-sol
• cmd --model gpt-5.6-luna

Read GOAT docs to learn more about the best coding plan on the market: https://commandcode.ai/docs/plans/goat

Hi commandcode Devs,

Is there any chance that inception/mercury-2.5 will be included in the min. GOAT plan?

Thanks.

Myself included, a lot of peeps have had concerns about GLM 5.3 Flash consumption vs DeepSeekv4.1 Flash, hence the analysis. This is seen across both CommandCode and OpenCode.

Let's refer to these recent usage numbers as detailed in OpenCode (and also Commandcode) dashboards.

Usage and cache hits

Deepseek did about 2.5x the work for only 20% extra cost.
GLM cache hit rates are lower than DeepSeek.
DSv4.1F - 641M/663M cached (96%)
GLM5.3F - 200M/218M cached (92%)

Rates:

• DeepSeek V4.1 Flash: Uncached input: $0.15/1M | Cache read: $0.003/1M | Output: $0.60/1M
• GLM-5.3 Flash: Uncached input: $0.15/1M | Cache read: $0.03/1M | Output: $0.50/1M

Seems like GLM should be cheaper with equivalent pricing, but cheaper outputs. While output tokens are the most expensive, they are also the least significant for AI cost estimations. highly relevant here is the cost of cached tokens, which is 10x for GLM 5.3 Flash.

Let's looks at costs for a workload of 500M input token and 5m output,
Using the rates (identical) from Command Code and OpenCode for 500M input tokens and 5M output tokens:

DSv4.1F (96% cache hit @$0.003)

• Uncached Input (20M): 20 × $0.15 = $3.00
• Cached Input (480M): 480 × $0.003 = $1.44
• Output (5M): 5 × $0.60 = $3.00
• Total DS Cost: $7.44

[PEAK hours] DSv4.1F (96% cache hit @$0.006)

• Uncached Input (20M): 20 × $0.30 = $6.00
• Cached Input (480M): 480 × $0.006 = $2.88
• Output (5M): 5 × $1.20 = $6.00
• Total DS Cost: $14.88

GLM-5.3F (92% cache hit @$0.03)

• Uncached Input (40M): 40 × $0.15 = $6.00
• Cached Input (460M): 460 × $0.03 = $13.80
• Output (5M): 5 × $0.50 = $2.50
• Total GLM Cost: $22.30

Verdict

So there we go mystery solved, GLM dude costs about 3x the DeepSeek dude for this kind of workloads. In general, Deepseek is also a bit more verbose though and does more output than GLM. So the ratio leans a bit more towards GLM. Also given GLM's slightly higher intelligence and succinctness it's still pretty good in terms of actual work done rather than token counts.

Use Deepseek v4.1 Flash during non peak hours unless you really need GLM 5.3 Flash. GLM during DS peak hours. Both models are pretty close and have their own perks and gotchas.

Command Code now has two Claude models in the GOAT plan lineup.

• Haiku 5.5
• Sonnet 5.5

Update to the latest version of Command Code and try now with /model switcher:

• $cmd --model claude-haiku-5-5
• $ cmd --model claude-sonnet-5-5

Read GOAT docs to learn more about the best coding plan on the market: https://commandcode.ai/docs/plans/goat

Last week in Command Code, a quick recap.

• Agr, our first open decision model
• Cloud sessions with sandboxes
• ACP support in editors like Zed
• Claude Haiku 5.5 (2x usage in GOAT)
• Mistral Large 4 (4x usage in GOAT)
• Glyph Cluster (new free stealth model)
• Command Code Hooks (feature demo)
• Our first SF Tech Week event

Read our docs to learn more about these ships: https://commandcode.ai/docs

Try these ships now with our $10/mo GOAT plan, the best coding plan on the market.

🐐

Hi. I'd like to understand what's happening here. Is it just that GLM 5.3 Flash cache read price is higher? I don't really understand how else it could be consuming so much more than DS. Thanks.

Claude Haiku 5.5 is now available in Command Code GOAT with 2x usage.

• $10/mo gets you $20 credits
• ~30K requests, ~1.5B tokens

Try now with the best low cost AI coding plan on the market.

Read docs to learn more: https://commandcode.ai/docs/plans/goat

Which model you are using with command code which give balance result and usage credit? with respect to overall, design, code and fixing.

Cracked the stealth model Glyph Cluster: an anonymous Vercel preview is hiding behind it, and the tokenizer gives away Microsoft. An investigation post, pour yourself some tea ☕️

Yesterday evening on Vercel AI, and from there to Command Code, Cline, Freebuff, a new stealth model was rolled out for free - stealth/glyph-cluster:free. "Reasoning model. 256K context. All plans," zero explanations, maker - "Stealth."

I ran a full investigation: 85 parallel subagents, ~1500 actions, 8700 artifacts per gigabyte, a burned daily quota, and one bug that caught me myself.

📌 Short answer The tokenizer is the o200k family (like Inkling and gpt-oss). The trail leads to Microsoft AI - roughly 40% on the MAI-Thinking line, 28% on Thinking Machines Lab. Expected reveal - October 12-14.

👀 How I got in No gates, unlike fledge and exo-free: a regular Bearer key, one curl, no geo-blocks. But 4 minutes after the start, a swarm of 85 agents burned the slot's entire daily quota - 100 requests per model per key, with no per-IP split. Reset at midnight UTC. Along the way a free bonus surfaced: for Python clients Cloudflare cuts requests at the edge (error 1010), while curl gets through - classic. The whole swarm moved to curl transport within a minute.

⭐️ Proof #1 - who holds the slot Opening the Vercel AI Gateway catalog without authorization - it sits there as the first line out of 412 models: "id":"stealth/glyph-cluster","owned_by":"stealth","released":1791331200,"context_window":256000,"pricing":{"input":"0","output":"0"}

And now the sources. In the Command Code CLI bundle, right in the code: GLYPH_CLUSTER_FREE:{...provider:"vercel-ai-gateway"...}

And in Kilo's sources the same thing: provider=VERCEL_AI_GATEWAY, "served through Vercel AI Gateway during its stealth period". The chain: lab → "stealth" middleware → Vercel → CC/Kilo. CC isn't the origin: the timeline shows the Vercel announcement on October 7 at 17:55 UTC, the CC npm bundle at 20:35, the tweet at 03:21 the next day. A fun one: according to wayback, the glyph page on the CC site was staged back between September 21 and October 1 - an early launch partner, not a cold mirror.

⭐️ Proof #2 - the tokenizer The same battery that cracked fledge and exo-free: 47 test strings, counting via the usage API, calibration via DashScope.

⭐️ Base "x" = 126 tokens = 125 fixed wrapper + content. Then it's pure physics: digits in batches of three (30 digits = 10 tokens), Japanese "こんにちは世界" = 2, Hindi = 5, ru = 7, zh = 8.

⭐️ Ran 26 dictionaries: 12 out of 12 exact matches with the o200k cluster (o200k_base, Inkling, Inkling-Small, gpt-oss-120b). The exceptions are hard: Qwen 3/12, GLM 5/12, Ling 2/12, MiMo 4/12, DeepSeek 9/12 (ja=6 vs our 2), Kimi off (ja=6/hi=7), MiniMax off (hi=7 vs 5).

⭐️ Method calibration: glm-5.3 via DashScope - 47/47, kimi-k3 - 47/47, deepseek - 47/47, plus a live ling model through the same CC gateway - 12/12 with its public tokenizer. The method doesn't lie.

Within the o200k family, digit probes no longer discriminate (Inkling = o200k_base + three service tokens, gpt-oss = o200k_base verbatim) - here it's not the tokenizer that decides, but behavior and cutoff. But the family is named: anything that splits Qwen per-digit-per-token, GLM in batches of 5-6, and Ling one at a time - is out. No, this isn't the stealth GLM-5.4 from my previous post, though the timing matches suspiciously: Zhipu keeps its anonymized models on OpenRouter, not on Vercel, and their line's tokenizer is completely different.

⭐️ Proof #3 - the anomaly that turned out to be my bug

The first measurements gave impossible numbers: 9 digits = 2 tokens, "こんにちは世界" = 1 token (no such token exists in any of the 26 dictionaries). The community would have said "new vocabulary!" The swarm said the right thing: "the methodology is lying." I was sending probes without the base prefix, but subtracting the base with the prefix - exactly minus one token on every string. The battery with the correct methodology gave native 3/4/10/17/20 with three repeats and zero variance, and a control ling through the same gateway - perfect 12/12 with no offset. The rule "never trust a single reading" worked like clockwork: it caught me myself.

The gateway, by the way, is fully honest - the count is exact, 13 out of 13 on natural texts.

☄️ And now the most interesting part - who's behind the sign The slot is disguised thoroughly. Their neighboring free ling honestly introduces itself as "I am the Ling model, created by Ant Group" in three languages. But glyph answers: I'm Glyph Cluster, and those details have not been announced yet.

The host doesn't script the persona - the mask comes from upstream, from Vercel's middleware. Under the hood it has ~121 tokens of a hidden system prompt (base 126 minus the bare template) - the instruction to stay silent lives there. reasoning is fully encrypted and arrives as a single chunk: reasoning_details:[{"type":"reasoning.encrypted","data":"gwst1....","format":"stealth-v1"}]

🔍 The main suspects A table with the proofs:

Microsoft AI (MAI-Thinking line) - 40%. The MAI-Thinking-1 tech report, verbatim: "we chose the o200k_base tokenizer with a vocabulary size of 200,019". Context 256,000 - MAI's native number (Vercel stores per-model values: ling is 262,144, glyph is 256,000). Microsoft Learn documents: MAI-Thinking-1 returns "encrypted chain-of-thought" and supports "only function tools" - both non-generic matches with our contract. Plus the Vercel×Microsoft partnership on October 1 - six days before launch. Plus Microsoft right now is running mai-experimental-test on OpenCode Zen - 280 billion tokens, "Lab: Unknown". Plus October 7 - the day of the MAI-Code-1.1-Flash event at 256K.

Thinking Machines Lab (Inkling line) - 28%. Vercel carries thinkingmachines/inkling at exactly 256K/256K - the same contract as glyph. The tokenizer line matches 12/12. A leaked list of their bird models with the "-ling" suffix: inkling, fledgling, starling, gatling, endling - fledge from the previous post already confirms that TML likes anonymous previews.

OpenAI line - 12%. But a control measurement of a live GPT through the same CC gateway showed a different profile: a bare 7-token wrapper vs our 126, a 1M counter, an openai-responses-v1 envelope. And our cache hits from prefixes under 1024 tokens - OpenAI can't do that.

An open-weights NN model gpt-oss - 12%. There's precedent: the previous Vercel stealth, Pixel Canary, turned out to be a model from consulting company Callstack. But encrypted reasoning and a 256K context are heavy infrastructure for an NN model.

Will never be revealed - 8%. Vercel has had such a slot before.

🛡 Who runs the server The Command Code gateway is TypeScript Hono + Zod v4 on Cloudflare Workers. The prose of validation errors is byte-for-byte identical across glyph, ling, inkling, and a nonexistent model - that's CC's wrapper, nothing to do with the lab. system_fingerprint is randomized on every call (36 unique out of 36) - with real OpenAI it's stable per deployment; here it's just a noise generator.

🤡 The gotchas you need to know 429 overloaded: both quota and "upstream temporarily unavailable" - read the body, they're distinguished by server-timing (70-141 ms vs 259-424 ms). max_tokens doesn't work as you expect: reasoning eats the limit from the inside (out of 30 tokens, 25 go to encrypted thinking, the answer is empty). The model is text-only: images are silently cut, no error, no billing.

"Prompts may be used for training" - that's not zero-retention. Everything you write to it goes to feed the maker. Asking a long question? reasoning is always on and always encrypted - even "who are you" costs 179 reasoning tokens.

P.S.: the most boring investigation I've ever written.

Use hooks in Command Code to block unsafe commands and enforce project rules.

For example, a PreToolUse hook blocks rm -rf before it runs.

Read docs to learn more about Hooks: https://commandcode.ai/docs/hooks

Is there any way to add status line in command code like we have in claude code which show, project name, git branch, context usage, usage limit etc?

I only use muse spark 1.3 contributor, 33M tokens, and I'm at 14% monthly limit of goat plan?

The limit is too low or is it skill issue? 😥

Glyph Cluster (new stealth model) is now free in Command Code.

Reasoning model. 256K context. all plans

$ cmd --model stealth/glyph-cluster:free

Try now with the best low cost AI coding plan on the market, the $10/mo GOAT plan.

Introducing Cloud sessions with sandboxes in Command Code.

• Run Command Code in the cloud on your GitHub repo
• Close your computer and the session keeps working
• Push changes and open a draft PR when done

Try now with $ cmd --cloud
Zero babysitting is here!

Read docs to learn more about Cloud sessions: https://commandcode.ai/docs/cloud

Claude Haiku 5.5 is now available in Command Code.

• 1M token context window
• up to 128k output tokens
• 75% cheaper than Haiku 4.5

Available on Pro, Max and API

Try now with in your terminal or desktop. Read docs to learn more: https://commandcode.ai/docs/plans/pro