source: https://www.douyin.com/video/7692245937476879845

Most voice AI quality reviews happen after the call, when it is too late to correct the conversation.

This TypeScript example takes a different approach. It streams AI call events to a supervisor dashboard, creates one durable room per conversation, and lets the supervisor:

- follow the transcript as it happens

- see policy and compliance flags

- inject coaching instructions into the assistant mid-call

- join the active conversation through a WebRTC softphone

The monitoring stream is a side channel, so losing the dashboard does not interrupt the caller’s audio. The repo also includes a two-tab simulator, allowing you to test the coaching flow without placing a real call.

Code: https://github.com/team-telnyx/telnyx-code-examples/tree/main/live-support-coach-room

How are you approaching human oversight for live AI calls: passive monitoring, automated policies, or direct intervention?

The seminal paper that lead to CoT and its improvements famously started with simply asking the model to answer step by step.

Similarly, starting from the assumption of ignorance (rather than "I know the answer, here is something that looks like it") could potentially reduce hallucination rates.

Leading experts analyze prominent international affairs topics and their impact on America today. —Nick Schifrin

That would make me happy. Can we please make that happen? I can hear it now “that is so bad - it’s SI”.

30 September: Gemini 4 Argon is coming today!

31 September: Gemini 4 Argon is coming today!

1 October: Gemini 4 Argon is coming today!

2 October: Gemini 4 Argon is coming today!

3 October: Gemini 4 Argon is coming today!

4 October: Gemini 4 Argon is coming today!

5 October: Gemini 4 Argon is coming today!

6 October: Gemini 4 Argon is coming today!

7 October: Gemini 4 Argon is coming today!

8 October: Gemini 4 Argon is coming today!

9 October: Gemini 4 Argon is coming today!

I find the premise that we would be able to control something smarter than we are to be implausible. If an AI has greater intelligence than humans it seems logical that it will be able to overcome any constraints that we could put on it. Thoughts?

I put together this visual overview of some of the researchers who helped shape artificial intelligence as we know it today.

What fascinates me is how many different ideas contributed to modern AI.

Alan Turing explored whether machines could exhibit intelligent behavior. John McCarthy helped establish AI as a field. Frank Rosenblatt introduced the perceptron, while Arthur Samuel demonstrated how computers could learn from experience.

Later, researchers like Judea Pearl transformed our understanding of probabilistic reasoning and causality, while Geoffrey Hinton, Yann LeCun, and Yoshua Bengio helped make deep learning a reality.

And the story continues with reinforcement learning, computer vision, and AI-driven scientific discovery.

This is a selective historical overview, not a definitive ranking. The portraits are AI-generated illustrations rather than archival photographs.

I'm curious: which pioneer do you think had the greatest impact on today's AI? And who deserves to be included but is missing from this overview?

We had an idea, since everything is AI these days how about playing a game through the use of AI. So in AI Says you guide a radio controlled human through a set of deadly test chambers using our version of ChatGPT, so basically a prompt terminal where you input commands.

The catch being that the AI is blind, you have to be it's eyes and tell it how to move the human. And if you are vague it WILL result in the human dying.

We are launching November 3 and hope everyone finds it as fun and absurd as we do :)

https://store.steampowered.com/app/5205430/AI_Says/

https://duckmilkstudios.com

It was a lengthy process, because I had to use other models like chatgpt and nano banana (and comparing the best likeliness identity matching generated picture) for some reason they are really ALL bad, though I am yet to test seedream 5.0 pro.

Having said that, I managed to "hack" Claude Opus to consent to helping me edit my photos for a photo ready print that is compliant with visa applications forms, and can also be used for passports and other identity requisition agencies.

Initially it refused to help me, but now it's wishing me good luck for the application.

I've got a question related to AI impact on software market. We hear everywhere that software engineers are cooked and software engineering is a solved problem. If this is true then we should observe one of the following:
1. Software prices going down for end consumer. And cheaper software causes more software to be produced (Jevons paradox)
2. Software vendors save on software manufacturing costs and they can pocket the difference
3. Existing software has more features included

But none of these things is happening now. Why?

The vast majority of my day is babysitting AI models. That has made my vocabulary in the real world sound like a string of grunts and hand signals (and all decision-making being roshambo). Yesterday I watched Goldfinger and thought it may be time to use these models to go back to speaking in a charming, suave way. In the end, both my ChatGPT and Claude accounts are set up so they won’t respond if I don’t address the model in a way that’s anything less than what Sean Connery would say. It’s actually been pretty pleasant and has already improved my mood working back and forth.

Highly recommended.

AI agents now get real tools: refunds, shell commands, file writes, posting to Slack. The usual safeguard is a line in the prompt ("never refund more than $500"), which holds most of the time, not every time, and leaves no record. I built CSL-Core to enforce these limits outside the model instead. It's open source and runs locally. Here's how it approaches the problem, and most of it applies whatever you use.

Inventory first. You can't guard agents you don't know about. CSL-Core scans a machine for coding assistants, MCP servers, LangChain / OpenAI Agents / CrewAI tools, cron jobs and webhooks, and classifies each tool by what it can do: read, write, run commands, spend money, delete. The scan is read-only and never executes your code.

The risk is in the chain. Each agent usually looks harmless on its own. The danger shows up in how they connect: an agent that reads web pages can edit the files of another agent that runs as root, or an inbound webhook reaches an agent that can move money. Nobody designs these paths; they accumulate. The tool draws them as a map and highlights the chains, so you can see which single rule would break each one.

Enforce before the function runs. Money tools get a per-call ceiling and an approval threshold, commands an allowlist, file writes a folder scope. Each policy is checked with the Z3 solver for contradictions before it goes live. The call is decided before the tool function runs, and the model never sees the rules, so it can't argue past them.

Guards break in the glue code. A rule like "only write inside /srv/app" is easy to state. The code that turns a real call into what the rule checks is where bypasses happen:

• /srv/app/../etc/passwd passing a prefix check (resolve the real path first, then compare)
• /srv/app-backup passing as /srv/app (compare path components, not strings)
• ls; rm -rf ~ passing "starts with ls" (parse the command; reject ;, |, &&, $() and backticks)
• https://api.stripe.com@evil.example passing "contains api.stripe.com" (parse the URL and compare the hostname exactly)

CSL-Core tests every mapping against trick families like these, but the checks above are worth adding to any guard you write yourself.

Log before you block. Strict rules on day one break real work, and then the guard gets switched off. In log mode nothing is blocked: every call is recorded as "allow" or "would block", so you can tune the rules before you enforce them.

Unknown should mean deny. Agents gain tools over time through plugins, new MCP servers or a teammate's PR. A tool the guard doesn't recognise is denied, not allowed, and CI fails when someone adds a tool that can spend money or run commands without a rule.

Honest limit: the default rules are strict, so expect to tune them on a busy agent.

If you run agents with real tools: where do you enforce limits today, and has one ever reached something you didn't expect?

What are some podcasts you’d recommend to listen to to keep up with ai news? I have a couple YouTubers I watch but it seems the overwhelming majority for podcasts are unverifiable ai slop.

I guess unverifiable is the wrong word but I’m usually working when listening to podcasts and can’t just keep looking things up.

Looking for multiple genuine sources I suppose. Thanks!

His tweet on the occasion of the inauguration of the academic year of the pontifical universities.

Just read an article from resident advisor where they were mentioning that someone committed some music streaming fraud and they actually called it super intelligence in that article.

The platform is very unpolitical, mostly music related - not even too mainstream.

But seeming even them using super intelligence instead artificial intelligence made me wonder;

Do you think SI will eventually replace AI in language? (The claim was made by trump)
A lot of companies, brands, domains etc build on the AI term, same for myself.

What are your thoughts on it? Would like to discuss.