GabrielBianconi

29 giorni fadiscuss

Thanks, appreciate the follow-up. It's certainly still to be determined if OSS AI infra will pan out, but I hope it does!

GabrielBianconi

29 giorni fadiscuss

Our investors aren't looking for safe, they're looking for a small chance in funding the next Databricks or similar. Most times it doesn't work out unfortunately, but that's part of the game.

(Also, we raised the capital in 2024 and didn't burn most of it.)

GabrielBianconi

29 giorni fadiscuss

Thanks, that's exactly what happened.

The title is misleading unfortunately but that's how social media goes...

GabrielBianconi

29 giorni fadiscuss

Our team was much smaller. We didn't spend all the capital.

GabrielBianconi

29 giorni fadiscuss

It's pretty common. If a startup winds down before it runs out of money, it typically returns whatever is left to the investors. We didn't have any debt.

GabrielBianconi

29 giorni fadiscuss

Thanks!

GabrielBianconi

29 giorni fadiscuss

See my sibling comment

GabrielBianconi

29 giorni fadiscuss

There are many factors at play here but if I had to pick one... an open-source company has to find product market fit twice: first for the OSS project and again for a commercial product. The AI market moves very quickly so it's easy to take a step in the wrong direction and fall behind.

I might publish a long-form reflection when the dust settles.

GabrielBianconi

29 giorni fadiscuss

We raised most of the capital before we had any traction. We raised on a rolling basis and had millions in the bank before we had even published the open-source repository. Ultimately we raised based on the team's background + vision.

The ~1% figure might be outdated today but it was a best-effort estimate a couple of months ago. TensorZero powered tens of trillions of inference tokens per month. TensorZero is not widely used but it was used by a couple of extreme-scale users.

GabrielBianconi

29 giorni fadiscuss

Yeah exactly. We didn't spend the majority of it.

GabrielBianconi

29 giorni fadiscuss

Mostly salaries to support a small team.

We are returning the remaining capital to investors.

GabrielBianconi

29 giorni fadiscuss

Turns out I was wrong :)

GabrielBianconi

29 giorni fadiscuss

Seed was in '24 actually but we only announced in '25.

GabrielBianconi

29 giorni fadiscuss

We raised in 2024 and only burned through ~$3m of it, mostly on salaries to support a small team.

GabrielBianconi

29 giorni fadiscuss

This was coincidental. Someone reported the issue last week, we fixed it, and published the advisory.

GabrielBianconi

29 giorni fadiscuss

I'm the co-founder and CEO of TensorZero.

We started the company two and a half years ago, and raised $7.3m in 2024 (announced only almost a year later). We've spent less than half of this amount.

Earlier this week we came to the difficult decision to wind down the project. The open-source repository remains available on GitHub (Apache 2.0) but won't be actively maintained by the team moving forward.

GabrielBianconi

mese scorsodiscuss

Any function that can score (i.e. "evaluate") your LLM system (e.g. your agent).

For example:

- You write a heuristic (regex, code, etc.) that assigns a score to an output

- You make another LLM score the output from your system (aka "LLM-as-a-judge")

- You have an automated system that can verify the generated outputs (e.g. does generated code compile or pass tests?)

People often talk about "LLM evals (evaluations)" which will include a set of evaluators i.e. scoring functions.

We'll make this clearer next time!

GabrielBianconi

3 mesi fadiscuss

It's getting more and more challenging to keep track!

GabrielBianconi

4 mesi fadiscuss

TensorZero works with the OpenAI SDK out of the box:

```

from openai import OpenAI

# Point the client to the TensorZero Gateway

client = OpenAI(base_url="http://localhost:3000/openai/v1", api_key="not-used")

response = client.chat.completions.create(

    # Call any model provider (or TensorZero function)

    model="tensorzero::model_name::anthropic::claude-sonnet-4-6",

    messages=[

        {

            "role": "user",

            "content": "Share a fun fact about TensorZero.",

        }

    ],

)

```

You can layer additional features only as needed (fallbacks, templates, A/B testing, etc).

GabrielBianconi

anno scorsodiscuss

TensorZero | https://github.com/tensorzero/tensorzero | Founding Member of Technical Staff | NYC (onsite) | Full-time

TensorZero creates a feedback loop for optimizing LLM applications — turning production data into smarter, faster, and cheaper models.

We're looking for a Founding Member of Technical Staff with any of the following skillsets:

‣ Back-end / Systems Engineering (Rust)

‣ Front-end / Design Engineering (React)

‣ ML Engineering / Research (Python)

What we offer:

‣ Vast majority of your work → open source

‣ Years of runway

‣ Small and entirely technical team: former Rust compiler maintainer, ML researchers with 1000's of citations, decacorn CPO

‣ $200-250k base + up to 1% equity + benefits

‣ Onsite (5 days) in NYC

More information: https://tensorzero.com/candidate-brief

Apply: https://www.tensorzero.com/jobs

GabrielBianconi

Submissions

Even (very) noisy LLM evaluators are useful for improving AI agents

Designing for Agents

We're building an automated AI engineer, and it works

Mitchell Hashimoto on Feature Design [video]

Bandits in Your LLM Gateway

Claude Plays Catan [video]

Is OpenAI's Reinforcement Fine-Tuning (RFT) Worth It?

How Kimi K2 achieves efficient RL parameter updates

Deploying DeepSeek on 96 H100 GPUs

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