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Launch HN: Hebbian Robotics (YC S26) – Build scalable robotics data pipelines

Hi HN, we’re Brandon and Kingston, the founders of Hebbian Robotics. We built HFlow (<a href="https://github.com/Hebbian-Robotics/hflow" rel="nofollow">https://github.com/Hebbian-Robotics/hflow</a>), an SDK that turns multimodal recordings from robots and human operators into standardized, quality-checked episodes and queryable dataset manifests. A recording can contain synchronized video, joint states, actions, timestamps, and metadata, and HFlow processes those streams together.<p>Here’s a demo of HFlow in action: <a href="https://www.youtube.com/watch?v=xni0GwV-xAw" rel="nofollow">https://www.youtube.com/watch?v=xni0GwV-xAw</a><p>Robotics data pipelines often begin as scripts: one transcodes video, another checks timestamps, another adds labels, and another copies selected recordings into a training set. This works until the corpus grows. Then it becomes difficult to know which code ran, why an episode was excluded, or whether a dataset can be reproduced. The first pain is usually quality control because frozen cameras, missing topics, timestamp drift, and duplicate recordings can quietly enter training data.<p>Brandon first encountered this while training embodied AI models for two-arm industrial cleaning robots. Kingston had run into related problems while building high-throughput infrastructure at Jane Street. Later, while speaking with robotics data providers, we kept seeing teams rebuild similar processing and quality-control infrastructure. We learnt that processing robotics data is itself one of the bottlenecks to improving robotics models.<p>An HFlow pipeline consists of transformations, checks, labels, and enrichments. The SDK exposes them as plain Python functions that receive an episode and return measurements, artifacts, or transformed data. During development, the functions can run in-process. For scheduled corpus processing, HFlow packages the same registered steps as Airflow 3 DAGs, where teams can inspect task status, logs, retries, and reruns.<p>HFlow currently accepts one MCAP file per episode. MCAP (<a href="https://mcap.dev/" rel="nofollow">https://mcap.dev/</a>) is an open container format by Foxglove for timestamped multimodal recordings, similar in purpose to a ROS bag. It lets video, robot state, actions, and other sensor streams remain synchronized in one file. We use it because HFlow needs to process these streams together, and because the resulting recordings remain compatible with Foxglove and Rerun. HFlow writes a canonical MCAP with in-band H.264 video, grouped camera and state chunks, and provenance describing how the output was produced. Each step has an explicit behavior version, and catalog records connect its measurements and artifacts to the source episode and pipeline run.<p>Quality checks store reusable evidence rather than imposing one universal definition of good data. Some failures, including black frames, frozen video, missing topics, timestamp drift, and impossible joint movements, can be measured deterministically without training a model. Others might be detected using VLMs and other models like MediaPipe Hands. But their meaning depends on the task. A smooth trajectory might indicate a successful demonstration in one setting and a stalled robot in another.<p>HFlow writes measurements, metadata, version stamps, and artifact locations to an append-only Parquet catalog. Teams query it with DuckDB SQL and produce a version-pinned manifest without opening the recordings again. Critical checks can quarantine an episode, but HFlow does not delete data. This separates the evidence from the policy used to assemble a particular dataset.<p>We did not want to replace the tools robotics teams already use. HFlow connects MCAP for synchronized recordings, Airflow for scheduled execution, Parquet for catalog data, and DuckDB for curation. Compared with a general workflow orchestrator, it adds contracts for robotics episodes, processing provenance, quality evidence, quarantine, and dataset manifests. Compared with a training dataset format, it operates earlier and stops at curated episodes plus a manifest.<p>Here are three examples of teams that would use HFlow: 1. A data vendor or marketplace collecting egocentric recordings. They could use HFlow to detect black or frozen video, duplicate recordings, hand-object interaction, and other quality metrics before delivering the data, while retaining evidence of which checks ran on every episode. 2. A robotics team collecting teleoperated demonstrations for its own models. They could use HFlow to standardize recordings, add labels and enrichments, and produce a reproducible training manifest. 3. A team operating robots in the field. It could process incoming logs, quarantine incomplete or corrupted episodes, and query the catalog for particular robot versions, environments, or failure conditions.<p>The project is pre-v1, but the core lifecycle works end to end. You can try it without an account, Docker, or robot hardware by cloning the repository and following the quickstart.<p>HFlow is free under the Apache-2.0 license. The open source deployment is currently a single-tenant workspace, and we have not built the hosted, multi-tenant control plane yet. We are considering making money through managed workspaces and enterprise support for teams that do not want to operate the runtime themselves.<p>Because this processing layer is software and data, people can contribute without owning a robot. We would especially like feedback from people who have built pipelines for robotics, video, or other sensor-heavy systems. We are curious where our data model is wrong, which integrations are missing, and what would fail first on your workloads.

Ask HN: Are we normalizing surveillance in the name of safety?

Whenever these policies are introduced, there are also many people defending them.Many people do not really feel that their own rights are being violated.Until one day, the restrictions keep expanding and become something like a censorship system where almost anything can be punished.On mainland Chinese platforms, if you try to have a deeper discussion about certain topics, or even just ask for opinions, it is very easy to get banned.Right now, fortunately, there are still many people around the

Ask HN: Why isn't there a cursor for video games development

So I was prototyping with claude code and Godot engine, and I thought it was weird that this specific category of AI isn't as big as video gen creative suites. Like theres big names like runway and higgsfield but when it comes to video games there's no major company trying to make a one stop shop using AI to make video games. I'm sure it isn't because of a lack of trying, I'm sure developers thought about it as one of the first things to automate.If I had to guess there&

Launch HN: RonanRX (YC S26) – Personalized Peptides and GLP-1s

Hi HN,<p>I’m Lloyd, one of two founders of RonanRx (<a href="https://ronanrx.com/">https://ronanrx.com/</a>). We are building a vertically integrated pharmaceutical company with software for prescribing, telehealth, compounding, manufacturing, and delivery. We are starting with GLP-1s and peptides.<p>The path to RonanRx is fairly unconventional. During the pandemic, I built one of the largest mask manufacturing factories in the US. We could make a million masks in a day. We converted raw polypropylene pellets into finished pallets of masks.<p>I had a background in software from my YC W10 company, so we took a software approach to building our factory. We used machine learning and computer vision to optimize production. We also vertically integrated almost everything. It worked. We built an efficient manufacturing operation and grew the business to 50 million dollars in revenue. Then people actually stopped caring about masks. Fair enough.<p>Around the same time, I decided to try and lose the weight I had gained while building the factory. I ate nothing but chicken for six months. While this diet got some results, I plateaued. My doctor suggested tirzepatide, a GLP-1 medication.<p>For me, losing weight was only the beginning. "Food noise" was gone. I showed less interest in other compulsive behavior. I stopped scrolling Instagram and quit biting my nails. But that was just the beginning. I was born with a congenital heart defect called aortic stenosis. My whole life doctors told me I'd need open heart surgery in my 40s (I am 45 now). After just 18 months using tirzepatide, my cardiologist’s began to think (and still thinks) my surgery could be pushed back until I am 70 or 80.<p>This was all happening when I was deciding what to work on next. I wanted to keep working on manufacturing. I didn't want to build another product that could be replicated by a frontier model. Pharmaceutical manufacturing was the best combination of digital, physical, biological, and regulatory infrastructure I could find. The thing that bothered me was how disconnected all of the pieces were: telehealth, EHR, prescriptions, dispensing software, production, shipping. No one company has it all.<p>GLP-1s come in a handful of predefined doses, but patients obviously aren't standardized: they respond differently, lose weight at different rates, experience different side effects, and may need very different doses. Compounding pharmacies can fill customized prescriptions, but they are not built like software companies. Workflows are manual, fragmented, and disconnected from the patient’s data. It's our goal to connect those pieces with RonanRx.<p>Patients either come to us directly or are referred by a physician. Once a patient is in our system, we collect medical records, current meds, labs, and data from wearables. Our software follows the prescription through drug formulation, compounding, quality testing, and drug dispensing and distribution.<p>The most interesting feature is the feedback loop. As the patient is undergoing treatment, their response becomes data that can be used by their doctor to modify the treatment. We keep the doctor in the feedback loop.<p>We make money by selling drugs we manufacture. Because people can buy directly from us, their price is usually 3x-10x more affordable. We can do this because we start at the molecule and own the entire stack from manufacturing through prescribing, dispensing, and delivery, instead of paying a chain of middlemen at every step.<p>We are beginning with GLP-1s and peptides since this was where I personally experienced the issue; however, we are more interested in the potential of patient-specific pharmaceutical manufacturing.<p>We would love your feedback, especially if you have built pharmacy, manufacturing, clinical infrastructure, EHRs, or any other regulated software. There are a lot of assumptions in this model that should be tested.

Tell HN: The Thoughts and Woes of a Full-Time Founder Part 1

My discussion of subheaders are open-ended questions with context, not answers or solutions.Combatting Shiny Object SyndromeShiny Object Syndrome official definition: the habit of getting distracted by new and exciting ideas, tools, or projects and abandoning your current workPerhaps due to AI, there are too many opportunities and too much breakthrough innovation happening right now. Suppose there is a crowded indoor gymnasium of 500 people, the bleachers are full upon first glance, and each per

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People Are Furious With This TV Presenter For Making Her Kids Slum It In Coach While She Flies Premium

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