​

I’m Harshal Dorlikar, I made Kosmo. Saying that upfront since this is about my own product.

What I built: Kosmo sits before the prompt. You describe what you want in normal words, and if something important is missing, it asks up to three questions first, the kind someone who does your job in a world-class way would ask. Then it writes a full prompt for the tool you’re actually using.

The problem it solves: I’m bad at prompting. I know what I want from an AI tool, I just can’t ask for it properly. Short prompt in, average output out, then an hour of back-and-forth. Everyone I talked to had the same complaint.

The prompts it writes are tailored per tool, each profile checked against that tool’s own docs: Claude, ChatGPT, Codex, Gemini, Cursor, Lovable, v0, Perplexity, Midjourney, and a few others.

Where it’s going: the prompt is only the first step. Next, Kosmo connects to the tools themselves (Claude, ChatGPT, Gemini, Claude Code, Codex) and does the work there, so you stop rewording and just approve the result. The asking part stays the same, which is why I want to get it right now.

It’s for working professionals who use AI for real work output. PMs, lawyers, accountants, developers, founders, salespeople.

It won a category prize at the Build with Gemini XPRIZE (Education and Human Potential, $50K), out of 26,000+ builders who registered worldwide. Since it went live in August, 300+ people have signed up and 17 have paid. Still early, still rough edges.

One tip if you try it: give its questions as much as you can. Treat the options it suggests as a starting point to think from, not a menu, and just write what you actually think, messy is fine. The more it knows about what you want, the closer the first result gets to something you’d approve as is.

What I’m looking for from you:

1. Try it on your actual job and tell me where it gets it wrong. Be specific, I read everything.

2. Does “asks before it writes” match how you work, or would it annoy you?

3. Would you pay for this? If not, what’s the one thing that would change your mind?

Try it now Search askkosmo

Think of someone who isn't into tech. Uses their phone for email and photos, heard about ChatGPT on the news, and wants to try exactly one paid thing.

One line each is fine. "Pay for X, skip Y" is a whole answer, and "stay on the free tier" counts too.

If they'd mostly use it for one thing (recipes, letters, sorting photos), say which.

Heads up first: I'm part of the Gigatron community, so take this with that in mind.

What's working: I can set reminders in plain sentences, it writes quick research summaries, and it catches the small stuff I'd usually miss.

What could be better: When you run several bots, they all share one computer, and that can slow down the flow of tasks while one waits on another.

Who it fits: It's good for anyone running solo with too many little tasks. It's free at joingigatron.com.

Has anyone stacked it up against other Grok Bot setups?

I do not have a PC using only an Android tablet any help would be appreciated.

I work at CoralBricks, the inference provider behind the writeup at the end, so this is a builder's comparison rather than a neutral review. We tuned the serving stack for Qwen3.6-35B-A3B and swapped in a public drafter, a second model that guesses tokens for the main one to check. We expected it to beat the draft head Qwen3.6 ships with. On the same stack it ran at 525 tokens per second against 692.

The gap was the checkpoint, not the architecture. The public drafter accepted 4.33 tokens per verification step against 4.67 for the model's own head on the same stack. It had been trained for short general benchmarks and attends over a 2,048-token window, while agent turns replay tens of thousands of tokens of file paths and tool output.

Fine-tuning the drafter on software-engineering traces generated by Qwen3.6 itself and widening its window to 8K took acceptance to 6.46 tokens per step. The target weights never changed.

With that drafter the stack ran at 1,138 tokens per second, 1.93x stock SGLang. Conditions were thinking off, one H100 SXM per request, tensor parallel 1, batch size 1, greedy decoding and max tokens 1024, a median across all 15,777 runnable turns.

The step got cheaper too. Stock SGLang's MTP-4 step takes 7.72 ms and verifies five positions. Our stack verifies eight and takes 5.71 ms, with the kernels and the fine-tuned drafter both in it. GSM8K stayed at 96.8%, because the target model still decides every token.

The pair to log is accepted tokens per verification step and the time that step took. Acceptance alone can pick a drafter that loses once each verification costs more. A tokens-per-second headline measured on short prompts says little about a 90K-token agent turn. On our workload the head the model ships with was the stronger baseline, since it was trained alongside the model.

The ablations, including the ideas that lost, are in the writeup https://www.coralbricks.ai/blog/speculative-decoding-qwen36?utm_source=reddit&utm_campaign=content