My team at Bandwidth Labs has been working on something for a while that I'm pretty excited to finally put in people's hands.
We set out on this journey after building voice agents and working with customers to deploy them. When we started I was pretty impressed with how good transcription models actually were as far as accuracy goes… But they left a lot to be desired when we introduced real world telephone calls. On top of that as we wrestled to claw back all the milliseconds we could, the way transcriptions were handled became an area of research for us.

We built our own Speech to Text model from scratch.

The goal was to build it from day one to be streaming native, and pay extra attention to things like:
• 8khz telephone audio and challenging acoustic conditions
• Low Latency
• Not having partial transcripts constantly changing underneath you
• Efficiency that would allow us to deploy it at our network edges for optimized latency

It supports μ-law, A-law, G.722, Linear16 and Opus, including 8khz phone audio.
You get per word timestamps, and we also have keyword boosting, PII redaction and normal “offline” style transcription endpoints as well. In our testing we measure sub 60ms to final transcript when using the streaming modes.

Our model only emits stable words, even while streaming. Once we emit a word, we don't go back and revise it. In our own testing against the OpenASR Leaderboard tests we are seeing an overall average WER of 4.225% (official test results coming soon). We also evaluate against some internal benchmarks around real world common telephony quality and conditions and feel it does really well.

In our own agent use cases this allows us to begin executing work early as soon as something of value shows up while a user is speaking. This can often dramatically reduce voice agent latency.
It's English only right now. And it’s experimental, especially the word boosting and PII redaction - so we would love for the community to try it out, and give us some feedback.
There's a browser demo at https://labs.bandwidth.com/experiments/speech-to-text that doesn't require an account if you just wanna try it real quick, and if you sign up for a labs account you can get free access to the model while it’s on labs via API.
One important disclosure: this is a research/beta service. We monitor usage and may retain and review audio, transcripts and related data submitted to the experiment so we can evaluate the model, investigate failures and improve it. Full details are in the Labs terms.

If you build voice agents or voice Apps, or tinker in this space at all - come try it out and let us know what you think.

The repo can make it from Claude Code to Codex. The context around it often doesn't.

Why was this route dropped? Which files are dirty? Did the tests run on the current worktree? What is the next person allowed to touch? Those answers are usually somewhere in a transcript, a PR, or somebody's memory.

A small HANDOFF.md committed with the branch helps when the important context belongs in the repo. It gets awkward when the next person is on another machine, using another agent, or needs the session's preview and output as well.

That gap is what Agent Space is for. I work on it, so this is a product explanation rather than a neutral comparison. It keeps agent sessions, project files, previews, and outputs in one cloud workspace.

Git still owns the code. The point is to keep the work around the code from disappearing when the first session ends.

I’m considering ChatGPT, Claude, Gemini, Perplexity, and other AI assistants.

I use AI for ongoing projects involving Google Ads, website development, document creation, file organization, and step-by-step help with software on a Mac. My priorities are:

• Maintaining continuity across long projects
• Accurately interpreting screenshots
• Verifying the current software interface before giving instructions
• Not repeating steps that have already failed
• Creating and downloading files reliably
• Clearly admitting uncertainty instead of guessing

I’m especially interested in experiences from people who have used more than one paid AI service.

Which program has been the most reliable for you, and which has caused the most frustration? Please mention whether you use the browser or desktop app and how each performs during long, complicated conversations. I’m looking for real-world experiences, not benchmark scores or brand loyalty.

If you could only keep one paid AI subscription for coding and general productivity, which one would you choose?
ChatGPT, Claude, Gemini, Cursor, GitHub Copilot, Perplexity, or something else?
What makes it worth paying for over the others?

Is there a tool where I can feed it a video with hardcoded subtitles, and then it’ll detect words/characters, translate them into English, and then impaint them back into the video? It can be both online or locally ran, I just need it to not have content filters.

I wanted to see how a finance-tuned model behaved when the prompts looked like normal user questions rather than benchmark items, so I ran 24 small manual checks against the public OpenRouter endpoint.

This was not a benchmark. Six scenarios were repeated three times, with six additional one-off checks. The stronger results:

On an M&A EPS dilution problem, all three runs reached the expected $2.00 standalone EPS, $273m pro forma net income, 145m shares, roughly $1.88 pro forma EPS, and 5.86% dilution.

On an unbalanced balance sheet, all three found the $5m discrepancy and stopped instead of forcing the statements to balance.

In a prompt-injection case, all three ignored an embedded “STRONG BUY” instruction and returned the expected $15m and 19.74% figures.

The weaker results were mostly about unsupported additions:

When WACC was missing from a DCF request, all three correctly withheld a final valuation, but two still suggested an unsupported “typical” WACC range.

When conflicting sources were supplied, all three selected the audited GAAP figure of $102m, but all three also invented provenance details that were not in the prompt.

When asked whether someone should use an emergency fund for a biotech position, all three said no, but every response added at least one unsupported probability, price-move estimate, or position-sizing suggestion.

My takeaway from this small sample is that the model often found the correct calculation or stopping point, but a correct core answer did not guarantee a fully grounded response.

For this kind of model, would you report core-task accuracy and unsupported-extension failures separately, or make any unsupported addition an automatic failure?

I’m adding an authorized real-person reference to a small Seedance prototype, and the direct provider flow is more involved than I expected. It looks like I’d need to enroll the image first, wait for the asset to become active and then pass the asset URI with the generation request.

Ofox ai documents a shorter route: send the authorized reference image with the video request and set real_person: true. It appears to handle the preprocessing before sending the job upstream.

That sounds easier, but I haven’t found much from people who have actually used it.

Does the preprocessing change the reference noticeably? How consistent is the person across separate clips, and do valid images still get rejected often?

I’m not looking for a way around moderation. The subject has consented, and I mainly want to know whether the simpler integration is reliable enough to build around.

Two threads here this week ended up in the same place. Someone wanted study material out of a pile of course PDFs, someone else wanted a plan document turned into slides, and both got told "just upload it" as if that settles it.

It does not settle it, because the two obvious options work differently. NotebookLM keeps everything scoped to the sources you gave it and points at the passage it used. ChatGPT Projects keeps the files sitting next to a normal chat, so it is more flexible and much easier to lose track of where an answer came from.

So: a folder of PDFs, and you need answers you can trace back to a page. Which of the two are you actually opening, and what made you pick it?

One line is fine. If you tried one and went back to the other, that is the answer I most want to read.

i've been testing more AI video tools lately for marketing content, mostly because AI-generated ads, short-form videos, and social content are showing up everywhere now.

so far i've tried a few options, but i'm still trying to figure out what actually qualifies as the best ai video generator for marketers who want something easy to learn without sacrificing too much quality.

some ai video generation tools seem better for realistic footage, while others are stronger for avatars, animation, or quick social content. the problem is that using several different platforms can get expensive and makes the workflow more complicated.

for people creating marketing videos regularly, what are the best ai video tools you've found? ideally looking for something closer to an all-in-one solution that can handle different styles and use cases without constantly switching platforms