Rhythm Garg

Today Hone launches Engines: AI that owns business outcomes.

Chatbots give answers. Agents do tasks. Engines deliver outcomes, and we hold them responsible for creating real value, measured in actual dollars instead of tokens or vibes.

You point it at a business metric, and the Engine figures out the rest. It onboards itself, builds the necessary agents, skills, and memories, collaborates with the right people, and gets better with experience.

From AI leaders to global enterprises, our customers have already used Engines to:

• Uncover millions in procurement savings
• Optimize product funnels
• Manage credit risk
• Increase qualified pipeline in new territories
• Accelerate implementations and improve account management

To scale this impact across the economy, we’ve raised a $60M seed led by Benchmark and Index with participation from Elad Gil, Hanabi, Definition, Diffusion, Lux, SV Angel, and Align.

AI’s raw capabilities are far ahead of the value it creates in the real world, and Engines will help close the gap.

Here’s how they work…

We're adding reward-hacking monitors into AC2. Every RL run gets checked for cheating, and our research agent, Ari, automatically investigates the flags.

After a model discovers an exploit, it can become its default strategy within a few training steps. We used to hunt for reward hacking manually, reading traces and running one-off agent queries. This was error prone and didn't scale. We decided to automate detection instead.

I implemented a system in @appliedcompute’s platform for automated failure mode clustering with Jev to surface errors at an even larger scale than before.

RL training produces billions of tokens in traces. I always manually read many traces to understand model behavior, but finding agent failures (like reward hacking / hallucinations) at scale is easy to miss without automation. Here’s how it works:

A 35B open-weight model trained to search a precomputed index answers repo search questions at 100x lower cost than a frontier model.

We partnered with @turbopuffer to train Qwen3.6-35B-A3B to find code across ~9,000 repositories. It tops the needle-in-a-haystack task outright at 2-10x lower latency.

Kimi K3 full fine-tuning is live on AC2. Our memory optimizations reduced GPUs required per training replica by ~40%.

At nearly 3T parameters, Kimi forced us to rethink how we manage memory, communication, rollouts, and checkpoints. The result is a much more efficient path to training frontier-scale open models.

Today, we're introducing AC2, the Applied Compute Agent Cloud, to enable every team to train, serve, and improve their own frontier models.

Update on @harvey’s model training effort.

We post-trained a model we are calling Tenet:
- Achieves SOTA on LAB
- Generalizes 3rd party legal benchmarks
- Uses sub-agents for domain specific capabilities

Tenet uses Kimi K3 as base and was post-trained in collaboration with @FireworksAI_HQ:
- Rank-64 LoRA over the full network
- GSPO with importance-ratio masking
- 134 B300 GPUs for 2 months

Despite not being trained on 3rd party legal datasets we found improvements on:
- @mercor’s Apex Agents - Corporate Law
- @crosbylegal’s Redline Bench
- LegalBench

Tenet also learned how to use domain-specific subagents (separate post-trained models) for complex tasks:
- M&A Diligence: training in an RLM harness for long-horizon tasks (with @baseten)
- Review Table: specialist models for high-volume structured data extraction (with @appliedcompute)
- Firm Knowledge: parametric memory and structured notes for more efficient enterprise search (with @engram)

These results suggest we can significantly scale training and we plan to:
- Scale both human and synthetic data significantly and scale training to 1K and then 10K GPUs
- This scale will let us move to full parameter fine-tuning and larger models
- We are now starting to post train models in our production harnesses
- Post-train other open-source base models to provide customers with model choice

If these problems sound interesting we are hiring for our post-training team

We partnered with @Harvey to train a model for Review Table, achieving state-of-the-art accuracy at a fraction of the cost and latency of frontier alternatives.

Review Table is one of Harvey’s highest inference volume products. Training an open source model for production traffic required building a representative eval and understanding how real lawyers use the product. Our teams discuss the research process.

At Applied Compute, we're not just training models, we're building a model factory.

To help researchers scale, we built Ari, our in-house AI research agent.

Ari diligently sifts through gigabytes of logs to find evidence of unhealthy behavior, remembers results the team has learned from past experiments, and produces styled research reports for the team to review.

You can now post-train a model inside your existing production harness with our platform, AC2.

A production harness is a whole engineered system around the LLM, with its own context management, tools, sandboxing, and control flows. Porting that into a new training runtime can be expensive and could introduce train-test mismatch, where the policy is optimized against a simulated harness and then struggles in production.

All you need to do is swap out the harness’ LLM response endpoint to one provided by AC2, and expose a lightweight protocol for AC2 to initiate and grade rollouts; the trainer handles the rest.

As a supporter of the open weights ecosystem, we're proud to be a post-training partner for NVIDIA Nemotron.

We post-train Nemotron models for customer use cases, de-risk mainline RL runs on our AC2 platform and training stack, and contribute aggregate workload statistics for inference benchmarking.

This is how open models get better, and we're excited to keep working closely with @NVIDIAAI.

Production traces contain rich feedback that rarely makes it back into training pipelines. Self-distillation methods allow us to close the continual learning loop and learn from every interaction.

We’ve added native support for OPSD and RMSD to our platform, AC2. Here’s a preview:

Most training frameworks only allow you to train on a single sequence of tokens. But enterprises orchestrate multi-agent systems and evaluate them based on task-level outcomes. We built our platform, AC2, around this premise.

A single task attempt can span subagent spawns, compaction, multi-response collection, and transient messages.

AC2 tracks all of it as one trace and trains on it with one task-level reward.