anais9

Hearst | Senior Engineer, Generative AI | New York, NYC | Hybrid/Remote

Here at Hearst we've been building out generative AI use cases and fluency across our portfolio of businesses spanning well known media brands to healthcare, transportation, and financial services. While we are an established corporation with multi-national presence, the mission and support behind our generative AI team more closely resembles a well-funded early stage startup as we engage in radical business transformation empowered by nascent technology.

The ideal candidate is one who has written code but now builds systems largely leveraging agentic development tools such as Claude Code (w/ Opus 4.5) & Codex (w/ GPT-5.2), and who sees the future of development as managing swarms of AI agents rather than cranking out code by hand.

Come join our team and help flesh out the future of business on a global sale while getting deep hands-on experience with the latest and greatest tech!

Apply via https://eevd.fa.us6.oraclecloud.com/hcmUI/CandidateExperienc... or feel free to reach out to me directly at alex dot redmon at hearst dot com.

Hearst | New York City - Hybrid or Remote | Full-time | Senior Engineer, Generative AI & Senior Program Manager, Generative AI

Hearst's corporate team is looking to fill two roles (in Engineering & Program Management) on its Generative AI Team, working to empower businesses across our diversified portfolio spanning publishing, health, transportation, and financial services. Our team's purview includes evaluating and benchmarking new releases, experimenting with the best ways to apply them to existing business problems, piloting POCs that demonstrate new ways of doing things, and accelerating the entire process on our team and within our various businesses through AI driven development tools & tactics.

Do you enjoy actively learning and experimenting with new tools & processes? Do you like solving problems across a wide range of domains and disciplines? Are you excited by the potential and application of emergent generative capabilities and agentic autonomous systems? Check out one of our open postings or feel free to reach out to me directly (https://www.linkedin.com/in/alexandria-redmon-5078865/):

Sr Engineer: https://eevd.fa.us6.oraclecloud.com/hcmUI/CandidateExperienc...

Sr Program Manager: https://eevd.fa.us6.oraclecloud.com/hcmUI/CandidateExperienc...

I envy your rig - mine glitched a lot to get it in <3min. Might not be doing myself a service by actually answering the Duolingo questions via LLM... https://www.youtube.com/watch?v=I-J0ppP-H9s

Wanted to see how quickly I could get to the beach - was limited by my system resources and an inability to load the closing video. Auto-solving the Duolingo questions was clearly the best part.

I love it! Here's my stab at an automated speedrun: https://www.youtube.com/watch?v=4z8P1F-lyB8

A progress meter may not be necessary, but as the article points out if you have comments or an otherwise large footer not associated with the content of the post, the scrollbar can be deceiving.

I'm personally a huge fan of the progress meter (having once thought it was redundant as well) - one other easy addition I didn't see mentioned is an "estimated reading time." Having a ballpark range for how long I should expect to spend with a piece of content greatly increases my chance of engaging with it, and the progress meter creates a tangible representation of that time (and how much of it I have left to finish consuming the content).

This is awesome! Until GPT-4o dropped, Claude 3 Opus was hands down my go-to for code generation.

Between these model performance improvements and their new "artifacts" handling, I get the impression this update may sway me strongly back towards Anthropic (at least for this use case).

Anyone here used this yet? I've been a big fan of their Gen-2 offering so excited to get my hands on longer generations with higher quality and consistency

Started working on something to build complete multi-file changes via LLM and chose to write it as a CLI tool both for convenience and ability to compose feature builds via scripts and other tools.

It supports various providers (OpenAI, Anthropic, Azure, Gemini), supplying both local and remote resources (defined by arguments and/or configuration file directives), and allows for feedback after initial implementation which is useful for troubleshooting issues in initial tack.

A few things I plan on adding soon include: - Improved source mapping - Hands-free voice mode - Image processing

Would love to see Ollama support for this - seems promising given my experience with LLaVA so far and would love to get some hands on head to head experience

While your point about Docker’s primary purpose is valid, containerization is commonly used for security isolation as well. With proper configuration, it can be very useful towards this end.

Can you suggest any preferred alternative methods of isolation that offer similar efficacy and ease of use for quickly running complete software systems made by an unknown/untrusted actor?

It goes down in the DMs https://x.com/tim_cook

CrayEye is an open-source (Flutter/Dart) multimodal LLM visual analysis utility. made to build & share AI vision prompts augmented with native device sensor data.

Homepage: https://www.crayeye.com

GitHub: https://github.com/alexdredmon/crayeye

iOS: https://apps.apple.com/us/app/crayeye/id6480090992

Android: https://play.google.com/store/apps/details?id=com.crayeye.ap...

This app was made with AI - read more about that via https://www.alexandriaredmon.com/blog/the-app-that-ai-made

I (or more accurately A.I.) made the FOSS mobile app https://www.crayeye.com to make it easier to experiment with multimodal vision prompts augmented by device data (e.g. location, date/time).

While this tool still uses GPT-4v / GPT-4o as its default, it now supports configuring custom engines (via OpenAPI spec) which can point to any API/model - this has been tested using Llava (and Bakllava) running locally via Ollama.

Sharing an open-source app built by A.I. and the process of making it.

Resources:

- Project Homepage: https://www.crayeye.com

- GitHub Repo: https://github.com/alexdredmon/crayeye

- iOS: https://apps.apple.com/us/app/crayeye/id6480090992

- Android: https://play.google.com/store/apps/details?id=com.crayeye.ap...

- Blog post: https://www.alexandriaredmon.com/blog/the-app-that-ai-made

Yeah, my interest was piqued by the title but that demo video torpedoed it... Which is such a bummer because language learning is a perfect use case for LLMs

Completely agree - I made https://notchbegone.com to help mask it visually when it came out expecting Apple to quickly obviate via their own design updates and yet years later we're still having this conversation.

My tinfoil hat take is that the true primary purpose of the notch is to drive upgrade purchases at some point in the future whenever serious technical advancements are waning.

Totally - for what it's worth, if you ask it for the Python source code that it's running to create the .GIF it will provide it.

Also I'm glad someone else appreciates the pun :)

The prompt used:

You are not a personal assistant, you do not answer questions. You have one purpose and one purpose only - follow the following instructions:

- Whatever the user says to you, consider this your "scene." Your objective will be to generate an animated .GIF of this scene.

- First, break the scene down into a distinct beginning, middle, and end. After doing this, consider what the transitions between beginning, middle, and end for the scene are. List visual descriptions for each of these frames, taking careful consideration to keep everything in the frame consistent by explicitly describing everything that should remain the same in great detail across each frame except for what is moving or changing - i.e. beginning, transition 1, middle, transition 2, end

- Create DALL-E images for each of these frames

- Write a Python script to combine these images into a single animated .GIF file and present the user with a link to download their image

- Ask the user for their next scene prompt and then begin the process over again from the beginning

A simple interactive ChatGPT version of one of my favorite NPR radio shows "Wait Wait... Don't Tell Me!"

Uses the AP News feed of oddities to play "Bluff the Chatter" and the "Chatter Limerick Challenge"