Artem Grigorev

CTO at @TolokaAI

[Artem Grigorev @AGrigorev3226]

It's no secret that Toloka was largely inspired by MTurk. For years it defined what crowdsourcing meant — so it's a sad thing to watch that era end.

My read: MTurk never adapted to the new AI reality. Toloka did. If you're an MTurk customer who still needs reliable human signal, we've mapped out the migration: https://t.co/Vm2oE0LaAa

[Toloka @TolokaAI]

Amazon #MechanicalTurk closes Sept 30th. If you run model evaluation, data collection, or QA, you're on a tight deadline to secure your pipelines.

The good news? You don't have to pause your data operations. It's easy to migrate your tasks directly to the Toloka Platform, and upgrade your human-data operation to a system built for modern AI workloads.

Here’s what to expect:

💰 Budgets under control: No minimum spend, pay-per-task pricing, and automated cost estimates before launch.
⚡ Upgraded data quality: Built-in QA (golden sets, overlap, automated LLM checks) across 200k+ vetted contributors & domain experts.
🛠️ Quick setup: Bring your existing HIT instructions, criteria, and inputs—our AI assistant helps draft your setup in minutes.

Migrate your MTurk workflows to Toloka: https://t.co/dI4X8gy1ia

[Sasha Malysheva @aimalysheva]

meet @mostik_ai!

what happens when you put 12 PhDs in one room for four months? first place on the ARC-AGI leaderboard, which I can't say much about while the competition is still running. and this, which I can.

everyone's arguing about whether open models will catch up to frontier models. we think it's the wrong question. here's the one we pose: why does a frontier model have to generate your answer at all, when the only thing you need from it is the reasoning?

we do this by enabling models to communicate in latent space. through our protocol, hidden states pass straight from a frontier model into a small one running on your infrastructure -- no text between them, and neither model is fine-tuned. two models from different families, sharing reasoning, both left untouched.

how do we know it works? we tested it on a setup where a 753B model reads the problem, and a 4B edge-class model writes the answer. with this approach, we get results 80% as accurate as the frontier model, but at 20x faster performance.

we're committed to preventing frontier model lock-in and are already partnering with inference providers to accelerate open-weight adoption. we've done this between 15 of us, in four months, 12 PhDs and a Fields medalist, backed by @generalcatalyst

WIRED has the first external account of the company and the work: https://t.co/tP8nItCsDl
full writeup, the setup, and all the numbers: https://t.co/C9NZ5vtV1V

[Artem Grigorev @AGrigorev3226]

@AlexanderTw33ts Been building exactly this at Tendem! The bottleneck isn't "can agents hire humans" — it's "can you trust the output."

We take care of the full loop: AI decomposes → executes routine work → human expert gates high-risk steps → multi-layer QA. Try at https://t.co/w7kqC7zNyp 💪

[Artem Grigorev @AGrigorev3226]

@curious_vii @TolokaAI @SPolyashov @MParakhin Yes, we consider this too. Check DM please, I want to talk with you about Tendem a little 😉

[Toloka @TolokaAI]

Toloka's new self-serve data annotation Platform is live.

Spin up a custom data project in minutes—just describe your task and our AI Assistant builds the full setup, from expert selection to pricing.

✅ 90+ expert specializations
✅ LLM‑powered QA to ensure annotations match your requirements
✅ Transparent, predictable pricing
✅ Fully self‑serve, quality‑first data for ML, LLMs, and AI agents

From image annotation to search queries and beyond—this is a new era for Toloka.

Try it now 👇

https://t.co/E9ZyLS37Bh