wakahiu

I had pointed out similar concerns a few days ago. https://news.ycombinator.com/item?id=42051857

At this point it’s more an experiment on the limitations that these creatures can be pushed to in the Lunar environment.

The last part, “because it will make money”, is entirely a valid reason to de-extinct animals that once already attracted human interest, but were lost due do it unfortunately. It’s entirely the premise of Jurassic park. People already spend money on safaris, so the premise is not far fetched.

One can argue that there’d be perverse incentives that would arise from this model.

Do such kinds of experiments confound our search for extraterrestrial life? Mars and moon missions could introduce tiny life forms, that could be released into the environment. Some of these are extremely hardy (such as tardigrades) which could then start proliferating when conditions are right.

This is common across East Africa, including Kenya where I’m from. The night ends at 6:00AM, with 7:00AM being saa moja (first hour) of the day. Similarly, the day ends at 6:00PM (thenashara). Intuitively, this clock makes much more sense than the English clock. There’s rarely confusion between times because it’s embedded in the language.

Should be back up. Thanks for your interest.

Reframe AI | REMOTE | Berkeley, CA, USA (HQ) | Full-Time | reframe.is Reframe combines a low-code interface with AI agents to automate data workflows end-to-end, accelerate queries, eliminate bottlenecks, enrich data, run analytics, generate insights, and optimize data management.

Our Company is remote with team members in multiple states/provinces. While our roots are in Berkeley, CA, we fully embrace remote work, and in-person collaboration is entirely optional.

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Experience working building AI Agents and fundamental understanding of pertinent concepts like RAG, React Agents and LLMs inner workings.

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Reframe AI | REMOTE | Berkeley, CA, USA (HQ) | Full-Time | reframe.is

Reframe combines a low-code interface with AI agents to automate data workflows end-to-end, accelerate queries, eliminate bottlenecks, enrich data, run analytics, generate insights, and optimize data management.

Our Company is remote with team members in multiple states/provinces. While our roots are in Berkeley, CA, we fully embrace remote work, and in-person collaboration is entirely optional.

* Senior Backend Engineer | Python - Remote

Experience working building AI Agents and fundamental understanding of pertinent concepts like RAG, React Agents and LLMs inner workings.

Email us: hireme-24-02@rfm.sh

As the parent comment says, LLMs are very good at giving plausible answers. Without checking each cell, then the data you eventually output becomes suspect.

We've been trying to solve this problem at Leaptable (https://leaptable.co/). The crux is that while LLMs are still a black box, transparency in the way AI Agents interact with LLMs is key. For instance, seeing the outputs of each step in a chain-of-thought sequence helps debug common fallacies in the way the LLMs reason and built trust.

https://github.com/peterwnjenga/leaptable

Care to explain the context and why you are keen on natural language inquiries? It seems to me like the example you gave is very simplistic and can be solved by a simple dropdown filter on Excel and or SELECT * WHERE clause in a database. Note, I'm not implying that you are wrong to use natural language, simply that there seems to be more to the question.

Here's the short answer - Retool - Anvil - Bubble - Airtable - Webflow

The reality is that you shouldn't be turned away from these tools simply because of the negative developer perspective they tend to have, but you should consider the practicality of your situation. What matters is that you are aware that these platforms have limitations, and that you solve the inherent business problem you are seeking to solve immediately. Additionally, always have a plan to eventually migrate out if the platform if the situation warrants it.

What I find paradoxical is that we SW engineers tend to neg on low-code tools, yet proclaim that code is simply a means to a business problem. We enjoy coding new projects, and hate integrating or migrating off shityly built applications.

The practical reality is that whether it's code you wrote or a no-code platform, getting your tool to work in the 99th percentile is always going to be the hardest part of the problem. In this case, using a no-code platform whose inner workings are obscured away from your is just as challenging as working with an arcane internal application whose original developers left the company a long time ago.

Disclaimer - I'm building Leaptable - a Low-code database powered by AI Agents. It's still not ready for prime-time and would love your feedback. https://github.com/peterwnjenga/leaptable

Work your network making it known you are available for hire. Don’t use platforms such a upwork, they are simply a race to the bottom.

This is very cool and I can see how it'd be helpful for users. How did you arrive at this feature? Slash command's discoverability is key. The commands serve as documentation and hints. Additionally, love the fact that users can create customized commands as shortcuts for frequent workflows or other logic. Reduces repetitive clicking/navigating.

As the builder of LeapTable (https://github.com/peterwnjenga/leaptable), an open-source low-code database powered by AI agents, one of the first pieces of feedback I received from early users was the desire for slash commands similar to Notion.

After evaluating different options, I settled on incorporating PlateJS (https://platejs.org/docs/mention) to enable @mention-style slash commands in LeapTable's React-based interface.

PlateJS proved to be a great fit for easily bringing robust slash command capabilities into our webapp. The @mention commands allow for convenient execution of actions, integration with external services, and automation workflows.

For any other React developers looking to incorporate flexible, Notion-style slash commands into their webapps, I highly recommend checking out PlateJS. It provided the perfect building block for enabling rich slash command interactions in LeapTable.

I can corroborate that text-davinci gives much better results than for tasks involving summarization or extraction of key sentences among a large corpus. I wonder what empirical metrics OpenAI uses to determine performance benchmarks for practical tasks like these. You can see the model in action for analysis of reviews here: https://show.nnext.ai/

[Disclaimer - I work at nnext.ai]

You provide both the prompt and response from ChatGPT. Our tool will then query the GPT API to retrieve the vector embeddings that corresponds to your question and answer, map them to lower dimensions and the display the result.

To demonstrate the power of vector embeddings, we built a visualization tool that lets you visualize question and answer pairs that you submit. The tool queries the GPT API to generate the embedding, performs dimensionality reduction using UMAP, and displays the result in 3D alongside other user-submitted questions and answers. You can select clusters of points to understand how ChatGPT associates different questions and answers, as well as select them individually.

Appreciate the feedback.

Found it: https://hbr.org/2016/09/know-your-customers-jobs-to-be-done

It was good read.

I was recently looking for designers for my company when I came across an interesting profile on Dribbble. I reached out and quickly scheduled a time when we could talk over zoom. At the meeting time, in comes this person who seems to have a strange-looking, silicone-like face. I was using my Zoom account (I rarely use other peoples zooms unless I trust them), to avoid situations like this. One thing I noticed is that when the candidate touched their face, their fingers would appear to sink into their skin - almost as if it were made of liquid. Secondly, their face appeared larger, lighter and smoother than their neck. I got spooked an immediately let the candidate know that I was not comfortable moving forward.

More interestingly, what exactly are them mechanics of getting a deep fake into video call? How is it possible that a what seems like a deepfake could make its way into my Zoom? Is Zoom enabling external plugins that alter video details?

https://www.dropbox.com/s/4hf9c9kg52nxal0/Screen%20Shot%2020...

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We're hiring for a few engineering roles, please shoot as an email if you are interested.

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You bring up two viable points of view.

Compliance is hard to get around. There are products that have done this by sheer force of their cyber security budgets such as large cloud providers which are HIPAA compliant. As a result, companies that require thorough compliance measures are simply not our target audience.

Your second point about performance is critical to almost all of our target audience. First of all, there are self-hosted versions of this such as Facebook’s FAISS (has GPU acceleration), Spotify’s ANNOY, Elastic search KNN search and Milvus. We plan to optimize performance using the latest ANN algorithms, caching, being as close to the wire (Non-REST) transmission of vectors, dimensionally reduction e.t.c. At the end of the day, our stack will probably not be as fast as a self-hosted version but it’ll confer other advantages that we hope ML engineers will find useful similarly to AWS RDS or DynamoDB.

Entire companies are built upon providing fast, managed services - Algolia, Firebase and Heroku to name but a few.

Is your point that the market is simply too small/doesn’t exist or that the value prop is simply too weak / defensible?

This is really cool. Thanks for the link.

How would you handle an online ML application where the set of embeddings is changing such as an image recognition app where images are constantly being added and need to be deduplicated?

Exactly, once you have latency-sensitive applications that read-write embeddings, then you have to rethink your vector storage system. Furthermore, when your dataset grows to millions of datapoints, having a system that scales according avoids downtime and costly query lookup operations.

Main thing is that NNext is fully managed - you don’t have to worry about provisioning servers, version upgrades and package installation/ dependencies. One of the main thing I’ve observed about ML engineers is that they typically don’t want to be encumbered by general software engineering / dev-ops tasks like platform management. They want to focus on data.

He ordered and paid for 2 people to be murdered. Do you still think Clemency should be granted?

This feels like a derivative product lacking originality. What’s your secret source or defensibility? Clubhouse will eventually launch an Android app, why would users gravitate towards your app?

Ability to cope with boredom and stress.

Well if it helps anyone:

https://timelyhealth.co/