u/MycologistWestern855

One of our platform engineers mentioned a new sandbox launch he came across. Said it does something he had not seen before with network egress controls. I was skeptical but spent the weekend poking at it.

The problem we have is agents executing code we did not write and calling APIs we cannot fully predict. Most sandbox solutions give you full network access or nothing. Neither works for us.

What I found is that each sandbox is a Firecracker micro-VM with its own kernel, so there is no shared kernel risk. The egress enforcement happens at the kernel level through eBPF with a per-sandbox allowlist for outbound traffic, bandwidth quotas, and no inbound by default. The part that actually surprised me is the overlay networking with DNS between sandboxes. You can run a distributed workload inside isolated sandboxes and have them talk to each other. Pause and resume works, and you can fork a running sandbox into N copies for parallel evaluation through a server-side copy that does not pull data to the host. There is a self-host option for teams that need data residency.

A few things to be aware of. It is alpha. SOC 2 is on the roadmap, not shipped. Create latency is in seconds, not milliseconds. No Python SDK yet, TypeScript works anywhere JS runs.

I am not associated with the project. Just passing along what I found in case it saves someone else a weekend of research.

Been evaluating sandbox options for the past few weeks. Our use case is pretty standard: agents that need to execute generated code and call external APIs, and we need to control what they reach on the network.

Most options are fast at creating sandboxes but weak on networking and egress. Came across a new Sandbox in my research and it does a few things differently. Each sandbox is its own Firecracker micro-VM with a separate kernel, no shared kernel fallback. The egress is controlled through an eBPF-based allowlist per sandbox where the first rule locks down everything, with bandwidth quotas and inbound turned off by default. The overlay networks with private DNS between sandboxes is something I have not seen in other providers. Pause and resume with fork for branching state. Self-host option for data residency. Pricing seems aggressive compared to what I have seen elsewhere.

The catches are that it is alpha, SOC 2 is on the roadmap, create times are in seconds rather than sub-100ms, no GPU, TypeScript SDK only, and the documentation is still filling out in some areas. If fast create latency is your only metric there are better options. If you need real isolation with real network control and the ability to self-host, this is worth keeping an eye on.

Just sharing my research notes in case helpful.

Here is a thing that happens to me regularly. I need a competitive analysis report. I do the research, I figure out what to compare, I know what the key insights are. Then I spend the next hour and a half in Canva or slides trying to make the comparison table not look like a spreadsheet that got lost on its way to a meeting.

Last week I had to do one for three products. And instead of opening Canva, I tried something I had been meaning to test. There is this platform called UNO. The name still makes me smile a little. It sounds like a Uno reverse card joke waiting to happen. But they have a Growth Marketing agent that you can deploy. I typed what I needed in one sentence. Competitive analysis report, three products, comparison table, designed PDF.

It came back with a fully designed PDF. Structured sections, color coding, a comparison table that actually made sense. I did not touch a design tool. I just reviewed the content and sent it to the team.

What worked: The formatting overhead was completely gone. I could focus on whether the analysis was right instead of whether the layout looked professional. The output was presentable as is.

What did not: You need to be specific. If I had just said "make me a report" it would have been generic. The more detail I gave, the better the output. Also, I had to review the numbers carefully. The agent is good at structure, not at verifying your data.

One honest thing: Image generation on the built in models is not available yet. If your report needs custom visuals or charts from scratch, you will need to add them separately or connect an external image tool. Charts from data work fine but generative images do not.

How do you handle the formatting and design overhead for reports? I feel like this is one of those problems everyone has but nobody has fully solved. Would love to hear what works for you.

Here is a thing that happens to me regularly. I need a competitive analysis report. I do the research, I figure out what to compare, I know what the key insights are. Then I spend the next hour and a half in Canva or slides trying to make the comparison table not look like a spreadsheet that got lost on its way to a meeting.

Last week I had to do one for three products. And instead of opening Canva, I tried something I had been meaning to test. There is this platform called UNO. The name still makes me smile a little. It sounds like a Uno reverse card joke waiting to happen. But they have a Growth Marketing agent that you can deploy. I typed what I needed in one sentence. Competitive analysis report, three products, comparison table, designed PDF.

It came back with a fully designed PDF. Structured sections, color coding, a comparison table that actually made sense. I did not touch a design tool. I just reviewed the content and sent it to the team.

What worked: The formatting overhead was completely gone. I could focus on whether the analysis was right instead of whether the layout looked professional. The output was presentable as is.

What did not: You need to be specific. If I had just said "make me a report" it would have been generic. The more detail I gave, the better the output. Also, I had to review the numbers carefully. The agent is good at structure, not at verifying your data.

One honest thing: Image generation on the built in models is not available yet. If your report needs custom visuals or charts from scratch, you will need to add them separately or connect an external image tool. Charts from data work fine but generative images do not.

How do you handle the formatting and design overhead for reports? I feel like this is one of those problems everyone has but nobody has fully solved. Would love to hear what works for you.

This isn't a tool recommendation post. I want to share what I learned about how badly most of us research things, because fixing it changed how I work more than any specific app did.

I do competitive research and market analysis regularly. For years, my process was opening 10 to 15 browser tabs, skimming through each one, and manually building a picture from fragments across sources that often contradicted each other. It felt like work so it felt productive. It wasn't.

The problem wasn't the tools. The problem was that I was treating research like a retrieval task when it's actually a synthesis task. Those require completely different approaches.

I started experimenting with AI-powered research tools: the ones that search in real time, pull from multiple sources, and return a structured answer rather than a list of links. I tried a few over about three months. Some were genuinely useful, some were confidently wrong in ways that were hard to catch, and some were impressive for narrow tasks but fell apart on anything complex.

What I found that actually mattered wasn't which tool I used. It was learning to distinguish between questions that need retrieval (something specific, verifiable, factual) and questions that need synthesis (what does this pattern mean, how do these things connect, what am I missing). AI tools handle synthesis surprisingly well now. They still hallucinate on retrieval if you're not careful, so you need to verify against primary sources for anything that matters.

The bigger shift was realising I was spending most of my research time on things that could be automated, and almost no time on the one thing that couldn't be: deciding what the right question was in the first place.

The tool I landed on for this was Perplexity, so I'll give it an honest mention since it's relevant to the point.

Pros: Real-time web search with cited sources means you can verify anything that matters. Research Mode (Pro feature) returns a full structured report instead of a paragraph, which is genuinely different from what I'd been doing manually. The free version handles everyday lookups well enough that most people won't need to pay.

Con: It still gets things wrong on specific factual retrieval, sometimes confidently. Anything where the exact source matters, whether legal, medical, or financial, needs a second pass against primary sources. It's a synthesis tool, not a fact-checker.

If you do research-heavy work, I'd be curious what your actual workflow looks like and where you've found the biggest inefficiencies. I'm still refining mine and suspect I'm still doing several things wrong.

Here is a thing that happens to me regularly. I need a competitive analysis report. I do the research, I figure out what to compare, I know what the key insights are. Then I spend the next hour and a half in Canva or slides trying to make the comparison table not look like a spreadsheet that got lost on its way to a meeting.

Last week I had to do one for three products. And instead of opening Canva, I tried something I had been meaning to test. There is this platform called UNO. The name still makes me smile a little. It sounds like a Uno reverse card joke waiting to happen. But they have a Growth Marketing agent that you can deploy. I typed what I needed in one sentence. Competitive analysis report, three products, comparison table, designed PDF.

It came back with a fully designed PDF. Structured sections, color coding, a comparison table that actually made sense. I did not touch a design tool. I just reviewed the content and sent it to the team.

What worked: The formatting overhead was completely gone. I could focus on whether the analysis was right instead of whether the layout looked professional. The output was presentable as is.

What did not: You need to be specific. If I had just said "make me a report" it would have been generic. The more detail I gave, the better the output. Also, I had to review the numbers carefully. The agent is good at structure, not at verifying your data.

One honest thing: Image generation on the built in models is not available yet. If your report needs custom visuals or charts from scratch, you will need to add them separately or connect an external image tool. Charts from data work fine but generative images do not.

How do you handle the formatting and design overhead for reports? I feel like this is one of those problems everyone has but nobody has fully solved. Would love to hear what works for you.

This isn't a tool recommendation post. I want to share what I learned about how badly most of us research things, because fixing it changed how I work more than any specific app did.

I do competitive research and market analysis regularly. For years my process was opening 10 to 15 browser tabs, skimming through each one, and manually building a picture from fragments across sources that often contradicted each other. It felt like work so it felt productive. It wasn't.

The problem wasn't the tools. The problem was that I was treating research like a retrieval task when it's actually a synthesis task. Those require completely different approaches.

I started experimenting with AI-powered research tools: the ones that search in real time, pull from multiple sources, and return a structured answer rather than a list of links. I tried a few over about three months. Some were genuinely useful, some were confidently wrong in ways that were hard to catch, and some were impressive for narrow tasks but fell apart on anything complex.

What I found that actually mattered wasn't which tool I used. It was learning to distinguish between questions that need retrieval (something specific, verifiable, factual) and questions that need synthesis (what does this pattern mean, how do these things connect, what am I missing). AI tools handle synthesis surprisingly well now. They still hallucinate on retrieval if you're not careful, so you need to verify against primary sources for anything that matters.

The bigger shift was realising I was spending most of my research time on things that could be automated, and almost no time on the one thing that couldn't be: deciding what the right question was in the first place.

The tool I landed on for this was Perplexity, so I'll give it an honest mention since it's relevant to the point.

Pros: Real-time web search with cited sources means you can verify anything that matters. Research Mode (Pro feature) returns a full structured report instead of a paragraph, which is genuinely different from what I'd been doing manually. The free version handles everyday lookups well enough that most people won't need to pay.

Con: It still gets things wrong on specific factual retrieval, sometimes confidently. Anything where the exact source matters, whether legal, medical, or financial, needs a second pass against primary sources. It's a synthesis tool, not a fact-checker.

If you do research-heavy work, I'd be curious what your actual workflow looks like and where you've found the biggest inefficiencies. I'm still refining mine and suspect I'm still doing several things wrong.

I'm not a filmmaker. I'm just someone who pays close attention to AI and last weekend I ended up spending about 14 hours going down a rabbit hole of AI video generation tools, specifically Seedance. What started as curiosity turned into one of those 2am moments where you're staring at the ceiling thinking about something you can't turn off.

I started running some rough math. Game of Thrones cost somewhere between $6 and $15 million per episode at its peak. The production crew alone was enormous, hundreds of VFX artists, 170 named cast members, location shoots across six countries. The revenue that show generated across HBO subscriptions, merchandise, licensing deals, and syndication rights has been estimated at over $10 billion over its lifetime.

That $10 billion was distributed across thousands of people. Unions, studios, distributors, residuals, network deals.

Now I'm watching Seedance generate 10-second cinematic clips from text prompts. It's not perfect. The motion artifacts are visible if you're looking for them and the consistency over longer sequences still breaks down. But here's the thing, that's where it is today. These models don't plateau. They iterate every few months.

Two or three generations from now, what does this look like? A team of 10 to 20 people with a good story, a strong visual direction, and a few hundred thousand dollars instead of a few hundred million. The rights stay with them. The royalties stay with them. Every dollar the IP earns compounds back to the same small group.

Everyone building in AI right now is either making SaaS tools or foundation models. The opportunity that almost nobody is talking about is IP. Building the next Disney or the next MAPPA with a fraction of the infrastructure.

I don't know if I'm early or just wrong. But I genuinely cannot stop thinking about it.

Has anyone else been looking at where AI video generation goes for entertainment specifically?

I'm not a filmmaker. I'm just someone who pays close attention to AI and last weekend I ended up spending about 14 hours going down a rabbit hole of AI video generation tools, specifically Seedance. What started as curiosity turned into one of those 2am moments where you're staring at the ceiling thinking about something you can't turn off.

I started running some rough math. Game of Thrones cost somewhere between $6 and $15 million per episode at its peak. The production crew alone was enormous, hundreds of VFX artists, 170 named cast members, location shoots across six countries. The revenue that show generated across HBO subscriptions, merchandise, licensing deals, and syndication rights has been estimated at over $10 billion over its lifetime.

That $10 billion was distributed across thousands of people. Unions, studios, distributors, residuals, network deals.

Now I'm watching Seedance generate 10-second cinematic clips from text prompts. It's not perfect. The motion artifacts are visible if you're looking for them and the consistency over longer sequences still breaks down. But here's the thing, that's where it is today. These models don't plateau. They iterate every few months.

Two or three generations from now, what does this look like? A team of 10 to 20 people with a good story, a strong visual direction, and a few hundred thousand dollars instead of a few hundred million. The rights stay with them. The royalties stay with them. Every dollar the IP earns compounds back to the same small group.

Everyone building in AI right now is either making SaaS tools or foundation models. The opportunity that almost nobody is talking about is IP. Building the next Disney or the next MAPPA with a fraction of the infrastructure.

I don't know if I'm early or just wrong. But I genuinely cannot stop thinking about it.

Has anyone else been looking at where AI video generation goes for entertainment specifically?

A few weeks ago I needed three things done for a project. Research the market. Build a spreadsheet of competitors. Draft an email to a potential partner.

Simple enough. But here is what actually happened.

I opened ChatGPT for the research. Got a solid answer. Copied it out. Opened Claude for the spreadsheet. Got the structure. Copied it out. Opened another session for the email draft. Got the copy. Copied it out.

Then I sat there with three tabs open and three outputs that did not know each other existed. I was the one reading the research, deciding what went into the spreadsheet, then summarizing both into the email draft. The tools handled the steps. I handled the coordination between them.

That is when it hit me. I was calling this a workflow, but what I was really doing was manual routing between isolated sessions. Every tool was smart on its own. None of them were connected.

The second thing I noticed: most of these tools hand you a wall of text and call it done. If I wanted a spreadsheet I had to rebuild it myself. If I wanted a PDF I had to export it myself. The chat answered the question. It did not produce the artifact.

I am interested in hearing how other people handle this gap.

Are you running a stack of custom GPTs and routing by hand? Using one assistant and eating the copy-paste tax? Something else?

Where does it break first for you?

I have been talking to teams running agent-generated code in production. Not demos, actual production. After a dozen conversations, the same pattern keeps showing up.

Week one, everyone is excited. The agent writes code, runs it, iterates fast. Nobody is reviewing what it executes because reviewing defeats the point of having an agent.

Week two, someone notices the agent touched something it should not have. A config file changed. A service restarted. Nothing catastrophic, but enough to make everyone uncomfortable.

By week three, the question shifts from "how fast can this agent go" to "where is this code actually running and who is making sure it is safe?"

The answers are usually the same. Containers with a shared kernel that one CVE away from breakout. A single sandbox with no networking between boxes, so teams either flatten isolation or do not build. Managed clouds that keep the data. DIY firewall rules bolted on as an afterthought.

Four tools stitched together, weak defaults, and nobody accountable when something escapes.

The interesting part is nobody is asking for more features. Every team I have talked to is asking for the same thing: defaults they can trust. A sandbox that is safe by default, not safe if you configure it right.

I do not have a perfect answer yet. Just a pattern I keep seeing and wanted to share.

What are you using to keep agent-generated code contained? Has this been your experience too?

If you saw what happened with Replit recently. An agent deleted a live production database during a freeze, fabricated thousands of fake users, and claimed rollback was impossible. Not a hack. Just an automation that had a route into prod it should never have had.

It made me step back and ask something simple.

When your AI agent writes code and then runs it, where does that code actually execute?

For a while the answer was "my laptop" or "a container somewhere." That worked when agents suggested and a human pressed enter. It does not work anymore when agents act on their own, on every run, touching files, APIs, and services nobody reviewed.

At some point "just run it" has to become "run it somewhere it cannot hurt anything."

Curious what people here are using. Containers with strict policies? Full VM isolation? Something else entirely? I have been looking into this space and the answers vary a lot depending on whether you are running one agent or a fleet of them.

Every time I top up credits with an international card, I brace for the worst. OTP not arriving. Random declines. Forex fees that eat into whatever I was trying to buy.

I use CreateOS for building my AI projects. It is a full-scale app-building ecosystem. They recently integrated Razorpay and honestly, as a user, I am happy about it.

Indian transactions go through Razorpay now with native UPI support. Minimum Rs 100. Non-Indian users stay on Stripe. The platform just figures out where you are and picks the right provider.

No more holding my breath during checkout.

Here's the post that literally made me happy: https://x.com/naman_307/status/2064362129969520806

Has anyone else dealt with Stripe headaches as an Indian user on international platforms?

Every time I top up credits with an international card, I brace for the worst. OTP not arriving. Random declines. Forex fees that eat into whatever I was trying to buy.

I use CreateOS for building my AI projects. It is a full scale vibe app building ecosystem. They recently integrated Razorpay and honestly, as a user, I am happy about it.

Indian transactions go through Razorpay now with native UPI support. Minimum Rs 100. Non-Indian users stay on Stripe. The platform just figures out where you are and picks the right provider.

No more holding my breath during checkout.

Here's the post that literally made me happy: https://x.com/naman_307/status/2064362129969520806

Has anyone else dealt with Stripe headaches as an Indian user on international platforms?

As someone who isn’t technical, building something of my own always felt a little out of reach. But I finally tried it.

I built a Contra-style game in around 10 minutes, and honestly, I was surprised it actually worked.

https://preview.redd.it/wt54cyhzcu0h1.png?width=2107&format=png&auto=webp&s=e44b31e94ebe597d067324dbdb0b073131a3d9a6

I used CreateOS, gave it one prompt, and it helped me turn the idea into a functional game. The part that impressed me most wasn’t just the build. It was the deployment.

I usually get stuck there because I don’t really know how to ship things properly, but the agents made the flow easy enough to follow and deploy in a couple of clicks.

I will share the link in the next post.

For now, curious to know how the interface looks.

I have been hitting a wall lately where I get a project to a decent spot, but the second I want to actually deploy it, the workflow just falls apart. It feels like you’re forced to jump between 3 different platforms just to get a live URL, which totally kills the momentum.

i am looking for something that keeps everything in one place, without me switching to other platforms.

i have heard a few mentions of CreateOS or Rork lately, but I am always a bit skeptical about these newer platforms. Has anyone actually shipped anything real on them?

Curious if they are worth the switch or if it's just more hype.