yosai

Cool. Hydron can index PDFs up to 5,000 pages. Roughly how long does it take to index a 5000 page PDF?

Thanks for validating the pain point..I was also using chatGPT.It's was a pain.We are building something really big to solve the problem of the system developers.Stay tuned.

Devin and Cognition are great as co-workers for application software. They're proficient in JavaScript and Python and excel at solving logical problems in software. However, they aren't trained in or familiar with system software, which typically runs on physical devices and requires verification against technical specifications for programming.

Hey cjtechie,It's not an another co-pilot.Existing coding co-pilot can only do code generation,code comprehension But not allow you to upload your technical datasheet or crash log etc.Most of the coding agents are SaaS based and SOC2, GDPR and even HIPAA certified. However, these certifications do not guarantee data privacy since they are relatively easy to obtain. Moreover, most SaaS make use of third party providers for different sub-processes (LLMs, embeddings, reranking…), resulting in private data being transferred and stored on numerous servers across the internet.privacy-aware coding SaaS services anonymize sensitive data before sending it to AI. This approach combines the ease of adoption and power of SaaS with data privacy. However, it comes with a single point of failure, which is the anonymization algorithm itself.We solve it by genarting coding datsets and finetuning a small open source coding model on-prem.You can compare the response in a side by side view to gpt-4o model in our platform.

Yes rnavi,I have the same experience as well.The complete system software development can be expedited using AI.Yes, we are doing private beta with a Japanese semiconductor conglomerate.We go with a philoshopy like your data, your model and your cloud.We create instruction dataset from their tech spec and code.Everything on-prem.We have partnered with AMD and Nvidia for on-prem GPU deployment.We help them selecting the right small language coding model and deploying it with securely.

Please visit https://h2loop.ai/ to know more about h2loop.ai

We use multimodal RAG and tools similar to unstructued.io ,We generate structured output and use LLM again to do the matching with our AST parsed source code.Now matching part is really complex and need manual inspection and validation.

Thanks shrianshag..You can try some sample Datasheets(pdf) and ask code generation questions.

Please visit https://h2loop.ai/ to know more about H2LooP

Please visit https://h2loop.ai/ to know more about h2loop.ai

We have a team of domain expert who do the vetting of the instruction dataset.We do typical RLHF(Reinforcement learning from human feedback) and connect back to our SFT(supervised finetuning) loop.That's why we name ourself as hardware and human in loop.Humans play an important role in ensuring quality and accuracy of our dataset.

Looks to be a great observability tool.Any specific reason to choose rust here?

Integrate with MS teams and it will be game changing..

We thought the same solution for YoBulk but intentionally did not use S3 and lambda because all cloud infra cost and maintenance cost associated with this solution.You can find the details in our blog https://www.yobulk.dev/blog/Scaling%20a%20CSV%20importer

yes textninja.YoBulk's vision is to automate the first mile data onboarding and cleaning through AI so that Data scientists are free from doing any mundane task of data cleaning.

Ohh Yes..You are spot on. It's there in our upcoming release.Stay tuned please.

icelancer,Right now YoBulk is flattening the CSV to JSON with key-value document DB format and storing in MongoDB.You can use GPT3 to create mongo queries to fetch the data.We will be adding APIs soon where you can make a query through GPT3 and fetch the data from Mongo DB.Keep a watch on YoBulk Git repo.

YoBulk's scope is much much bigger than a standalone CSV importer.We want to complete automate the CSV import workflow with an AI firstapproach.https://www.yobulk.dev/blog/Building%20an%20In-house%20CSV%2... gives an idea about the collaboration and workflow scope of YoBulk.

Yes.. we are the making the point very clear here that we are the open source alternative to Flatfile and it resonates with your thoughts also.Curious to know why the GitHub repo has put you in ambiguity.Happy to receive a candid feedback from you.

Thanks for your comment PaulHoule. Text CNN is the way forward for YoBulk also..We will be building a model for self correction of CSV error.It has to understand the context at each cell level and train the model accordingly.

Thanks keep following YoBulk..We will keep on doing some path breaking work in CSV importing space.

Yes it's definitely open source.On lighter note, until we build our own text CNN, we will use open AI to show the power and usecases of AI to our users.

some of the companies in this space>> https://flatfile.com/,https://www.oneschema.co/,https://www....

Yes, we have a workflow for your usecase.YoBulk can create a template or schema by uploading a CSV.We read some lines and create the schema.Right now we have not added AI for that.This flow is very handy for the usecase like when you want to upload a CSV file to hubspot or linkedin and want to do the data cleaning according to hubspot and linkedin defined template.People can use upload linkedin or hubspot template CSV to YoBulk and create a template.Now they can validate their CSV data against the YoBulk template before uploading to hubspot and linkedin portal.

Thanks for resonating the problem statement.Yes internal teams also face the issue.You Reply on the other team to clean the data.YoBulk is automating this workflow where both data donor and receiver solve the data errors in a much collaborative way.

Yes you are right..In YoBulk we flatten the CSV to a JSON schema store it in a document DB and do all the validations.Chunking the CSV and analysing the stream buffers for validation is giving us speed also.

Yes you are absolutely right..We need a solution beyond standard as 80% businesses run on CSV..

@nerdponx This is really a great question. We are currently using AI for schema generation as well as column matching. The column matching is done with the Dice's Coefficient in yobulk system. But with chatgpt's column matcher, we are leveraging the model of chatgpt to match the columns. Further, there is a roadmap for auto cleaning by keeping the historical records and bulding a model to sense the data type entered into the csv for the specific organization.We give the user final power to decide which if the GPT output is correct or not.Happy to engage with you on this topic.

Sure We will add it in README..we captured it in our documentation.Please have a look. https://doc.yobulk.dev/YoBulk%20AI/AI%20usecases