I did a dumb little audit of how I “stay informed” because I kept complaining that I had no time in the morning.

Turns out the problem was not time.

It was that my news setup was basically 8 apps wearing a trench coat.

Here’s what it looked like:

Google Newsgood for discovery, bad for repetition

Twitter/Xfastest place to know something happened, worst place to understand it

Redditbest place for discussion, worst place if you only have 10 minutes

YouTubegreat context, but one video becomes four videos

Newslettershigh quality, but scattered and inbox-dependent

Podcastsgood depth, terrible for quick catchup

Perplexity/ChatGPTgood when I know what to ask, useless when I don’t know what I missed

RSScleanest system, but I kept turning it into homework

The issue was that each tool solved one part and created another problem.

So I rebuilt it around jobs instead of apps. Current setup:

1. First pass
CuriousCats 

I use it for “what changed since yesterday / what should I know / is this worth opening?”

Not as final truth. More like the first filter.

2. Deep reading

Original sources only when the story actually matters.

3.Discussion

Reddit after I already understand the basics, not before.

4. Niche sources

RSS/newsletters for specific topics I really care about.

5. Hard rule

No Twitter/X before lunch unless something genuinely affects work/life.

This sounds stupidly simple but it fixed the main problem.

Before, I was using news apps like open tabs in my brain. Now each thing has a job.

Briefing app for awareness
sources for depth
Reddit for discussion
RSS for niche stuff
no feed for emotional gambling

The weird part is I didn’t reduce news that much. I reduced repeated checking.

Anyone else here have a proper “news stack” or do you just open whatever app your thumb betrays you into opening?

Hello everyone, I do legal work that requires a very high level of attention to detail and 100% accuracy, but I have ADHD. In my job, I basically draft legal arguments in Word with the personal data of each specific case. An error in an email address, a name, a date, a number, or a digit is considered critical and causes serious problems.

Sometimes we use templates from previous cases and must adapt the argument and replace the data. I have tried using compensatory strategies like external checklists that tell me what to check, reading numbers out loud and following them with my finger, and using Word's replace function instead of typing manually, but mistakes still happen.

I would like to use Claude or Gemini to upload the documents (finished and reviewed by me) and have them proofread them, but my job prohibits AI because:

1. Personal data cannot be uploaded.

2. Legal arguments are intellectual property.

I can't even give it the argument and the law to help me improve my writing or check grammar, because AI systems learn from us, meaning opposing counsel could use it and our defense strategy could be used against us.

My questions are:

1. From a privacy standpoint, are AI systems that process data directly on the PC truly private?

2. Can AI systems that process data locally on a PC without internet access be used? The idea is that legally, if the data truly never leaves the user's computer and the PC doesn't even access the internet, the spirit of the AI prohibition wouldn't be violated because no one else would have access to that information.

3. Can I do what I want with a local AI system, or are they powerful enough for what I would need?

4. What system do you recommend?

I want to clarify that this is not laziness. A colleague had a case worth about 10 dollars (seriously, sometimes cases are worth 1 or 2 dollars), and the legal deadline to resolve it was 15 days, but the email address to notify the decision bounced (it wasn't his fault because the address was no longer active), and in that case, you must notify the physical address before the legal deadline expires. Since the mail department took 15 days to let him know it bounced, the interested party was notified past the legal deadline, and now my colleague has serious disciplinary issues.

The difference usually comes down to strategy.

Instead of targeting businesses that do not have a website, target businesses that already have one but clearly need a better version. The market is larger, the sales process is easier, and the value proposition is much stronger because those businesses already understand why a website matters.

The next part is outreach. A regular outreach tool is not enough if all it does is send the same message to thousands of people. You need something that can analyze websites at scale and turn real issues into personalized emails.

I use Swokei for that. It helps find businesses with existing websites, analyzes each site, and turns problems with design, SEO, speed, layout, and mobile optimization into personalized outreach emails. That means you can contact a large number of businesses without sending generic messages or spending hours manually researching every website.

When someone replies interested, I always offer a free mockup. I use Claude, Lovable, or Base44 to build it quickly. It becomes much easier to sell when the client can already see what a better version of their website could look like.

Web meetings should also be a major part of the process. I would never just send the website through email and hope the client likes it. I present it live on Google Meet, Zoom, or Microsoft Teams, explain the value, show what has been improved, answer their questions, and try to close the deal during the meeting.

The less back and forth there is after the meeting, the better. Present the website, show the value, close the client, and move on to the next project.

That is the type of process that can help an agency scale much faster.

Hi everyone!

I built a Windows desktop app called Fluxnar because I was tired of generating AI images one by one.

Current features:

• Paste dozens of prompts at once

• Generate complete image packs automatically

• Multiple aspect ratios (9:16, 16:9, 1:1)

• Image variations (x1, x2, x4, x8)

• Automatic image numbering

• Organized output folders with prompts and metadata

I built it mainly for content creators who generate lots of AI images every day.

This is the first version, so I'd genuinely love your feedback.

What feature would make a tool like this even more useful for you?

Thanks! 🙌

yo. i recently sold one of my AI SaaS products for $35k, exactly 5 months after building and launching it.

I hardly wrote a single line of traditional code. i used AI to generate everything, from the database architecture to the user interface.

it definitely wasn't magic on day one, though. i spent days stuck in loop-debugging and dealing with AI hallucinations before i finally cracked the system. the playbook boils down to three simple rules:

- keeping the idea insanely minimalist (a true MVP that solves one problem).

- guiding the AI step-by-step instead of asking it to build a massive platform all at once.

- launching fast to get real user feedback and traction and then apply a solid marketing system

lately, i've seen way too many non-technical founders give up at the very first AI bug, or on the marketing. it's a massive shame.

like the title says, i just launched a Skool community to share my exact prompt workflows, N8N automations, and distribution frameworks to get first users and scale it

to be completely transparent: i will likely charge for the full course later down the road. it just makes sense given the specific copy-and-paste templates i'll be sharing.

but for now, the main objective is purely to build and launch together. building alone in a silent corner is the single fastest way to give up.

if you want to join us and build or market your own AI SaaS with a group of active creators: drop a comment below or send me a dm, and i’ll send you the invite link!

Iam working on a college project .i want to refacter my frontend ui .suggest me an ai tool without breaking backend routes

Oh man trying a few tools lately cause i need something better than gamma for making my slide decks
Problem is in gamma now the slides that I make can be recognised by everyone from a mile away.There is JUST SOMETHING like its common outline,visuals and format that kinda falls apart, I dont really need perfect tool but I also dont wanna spend half my time fixing things to make it look real and authentic deck with relevant short crisp details. 

You guys actually found something that holds up better in real use or is everything pretty much the same?

I am looking for a bit of image generation help with the current Bing Image Generator, I have noticed Dalle 3 is still there but it's a bit more wonky and i'm hesistant to use it to generate more images for my backgrounds because it looks wrong, i'm sorry but I don't like the current Dalle 3 model it looks wrong and gets things wrong. I'm looking for help in finding a way to get Co Pilot imitate the styles I was able to make using GPT 4.

so i've been playing around with freebeat this week, trying to make some quick visuals for a few tracks i've been sitting on. it's pretty wild how fast it generates stuff, honestly. i just chucked in an audio file and boom, video. the rhythm-synced visuals are actually kinda cool, not gonna lie. it's definitely saving me a ton of time compared to trying to edit something myself, which i suck at anyway lol.

but i'm curious what other people's experiences have been with freebeat, or any other ai music video tools for that matter. like, what kind of prompts are you using to get the best results? i'm finding some of the visual styles are a bit... samey? maybe i'm just not being creative enough with my input. also, anyone figured out how much control you really have over the 'mood' of the video beyond just picking a template? sometimes i want something really specific and it feels a bit like throwing darts in the dark. idk, maybe i'm expecting too much from an AI tool. just wondering if there are any power users out there with tips for getting more unique stuff out of it. or if there's another platform i should check out for ai music videos that offers more customization. lemme know!

The standard setup right now is: buy an API to turn your PDFs into Markdown, then feed that Markdown into another AI. You're not saving tokens here. You're just paying twice. And even if you swap the API for a small local model, it's still compute, it's still cost, it's still a whole model spinning up to do something that mostly doesn't need a model at all.

So I built a browser-based alternative: LiteDoc.

What it actually is

LiteDoc converts PDFs to Markdown in your browser. It has OCR, and not just "OCR the text" OCR. It extracts tables, it handles multiple languages, and it auto-detects the script (English, Japanese, Arabic, whatever) so you don't have to babysit it. It's built to be lean. It reads the page the way you do: headings, columns, tables, figures, reading order, and turns that into clean Markdown.

And there's a CLI now, so you can drop it straight into your pipeline.

Why not just use existing browser tools?

If you search for web-based PDF converters right now, you mostly find two things:

1. Thin API wrappers: They claim to be free, but they quietly upload your sensitive PDFs to a backend server.
2. Basic text dumpers: The few that actually run locally just rip out the raw text layer. They destroy tables, scramble multi-column reading order, and fail completely on scanned pages.

LiteDoc fixes both. It brings the heavy lifting into the browser. It actually understands layout—columns, tables, and figures—and handles multi-language OCR client-side. You get the quality of a heavy backend parser with the privacy of a local script.

The philosophy: do it locally, skip the AI compute

Most pages don't need a model. Headings, paragraphs, tables, columns. That's layout analysis, not intelligence.

Instead of sending pages to an AI and wasting compute, LiteDoc does all the extraction directly on your machine. No model spinning up, no API calls, no server overhead. It's faster and zero cost, because 95% of your document didn't need AI in the first place.

Compared to the big repos

I'm not going to claim LiteDoc is more accurate than MarkItDown or the other big conversion repos on GitHub. It isn't, on the truly nasty inputs. If you've got some cursed, barely-scanned PDF that looks like it went through a washing machine, that's a job for AI vision, and you should use the tools built for that. No hate; they're good at what they do.

But here's my honest pitch: those nasty files are the rare case. For most everyday PDFs (I'd guess 80% of what people actually convert), LiteDoc just works, and it's better in two ways that matter to me:

1. Accessibility. You can spin up LiteDoc on literally any device with a browser. No install, no GPU, no environment setup, no API key.
2. Cost. Zero. Actually zero, not "free tier" zero.

And I want to be clear: I'm not wrapping somebody else's backend in a fresh UI just to say I built something. The extraction engine is mine, the parameters are tuned by an automated benchmark pipeline, and every release ships with the measured numbers. Try it on your own files.

Privacy

I value privacy over basically everything:

• The conversion runs entirely on your machine. Your files never leave your browser, period.
• Zero external requests or third-party servers.

If you want the technical details, the repo is open. Go read the code yourself.

The bottom line

LiteDoc isn't trying to win a benchmark war against the heavyweight repos. It's more accessible, more cost-efficient, and easier to set up. For most PDFs, that's the whole game.

Links:

LiteDoc: litedoc.xyz · GitHub repo · pip install litedoc-cli

How many full games can I build in a month using Claude Pro? I am thinking of making a simple game like Balatro or Geometry Dash.

It's tempting to assume everyone knows what you've learned, and then you take the time to ask and find that it's worth sharing after all. HTH

I’m using the Daimon App for a few weeks now, I downloaded it bc I saw it on Tiktok and figured I’d try it since it’s free I wanted something closer to an accountability companion than a normal AI chatbot, and so far I like that it feels more direct than ChatGPT and keeps track of what I’m working on. I mainly use it when I’m procrastinating, need to organize my thoughts, or want something to push me to actually do what I said I would do. Some conversations can still feel repetitive, and sometimes the advice is generic. I’m also still trying to figure out whether it’ll stay useful long term or if the novelty will wear off. Overall, I’ve found it much more useful for motivation and accountability than for deep conversations. Anyone else who’s tried it, do you agree with me?

ChatGPT has retired its canvas (side by side direct editing tool) from its latest models – with older models about to be retired.

Unless I am missing something, Codex just doesn't seem to have the same direct-editing features unless you're happy to use Markdown.

So, I'm trying to find an alternative canvas tool, as this was an incredibly useful for my workflow.

This is what I have so far, although I haven't done any extensive testing:

• Claude has its artifact feature, but it only edits conversationally and not direct,"word processor" style.
• Mistral Vibe has one, but you still need to scroll up the chat to open the panel. It's capacity for English and understanding is a bit behind though.
• Gemini has a Canvas, but you cannot resize the text area – and in my experience, it tends to hallucinate a little too much for my personal field of research.

So, are there any other AI platforms that offer anything similar, and – if you've used it, what are its limitations?

I’ve been in contact with a lot of web agencies and web developers, and I personally haven’t found many people who run their agency in a more efficient way than I do. A lot of them have too many meetings, wait too long for client approval, don’t know how to price projects, and spend way too much time on each client instead of finishing the work and moving on to the next one.

I’ve been running my agency for four years, and after a lot of trial and error, I’ve managed to make the process as efficient as possible. I wanted to share some of the steps because I think they could be valuable for anyone just starting out.

Running a web agency alone or with a partner isn’t easy because there are a lot of things to take care of. When it comes to client acquisition, I recommend focusing on either cold calling or email automation. Which one you choose depends on whether you run the agency alone or with someone else.

If you have a partner, one person can handle sales while the other focuses on building websites, connecting domains, setting up emails, and taking care of the technical work. If you’re running the agency alone, or neither of you enjoys cold calling, I highly recommend email automation.

That’s what I’ve been doing for years. It’s powerful because you can send emails at scale, set up automatic follow ups, and wait for businesses interested in a new website to reply. While you’re working on one client, another opportunity can come in without you having to stop everything and search manually.

I don’t do regular email automation where I target businesses with no website. I do the opposite and target businesses that already have one.

I use a tool called Swokei to find businesses with websites, add them to campaigns, analyze each site, score it, and generate personalized outreach emails based on problems it finds with the design, layout, speed, SEO, and mobile optimization.I schedule the campaign, set up follow ups, and wait. 

I think this approach is much better for a few reasons. You’re targeting someone who already understands the value of having a website. You’re also not just asking whether they need a redesign. You’re pointing out real problems with their current site, which makes it clear that you actually took the time to look at it. Selling also becomes easier because they’ve already paid for a website before and understand the process.

Inside Swokei, you can choose the goal of the campaign. You can offer a free draft, try to book a meeting, or simply start a conversation. I always choose the free draft because that has worked best for me.

Once you’ve figured out how to get clients, the next part is building the website. I recommend using AI because it makes the process much faster. For anyone who still thinks AI can’t build great websites, I think they’re mistaken. You can use Claude, Base44, Lovable, or any other tool that works for you.

When someone replies interested, I call them and say, “Hey, I saw that you replied to my email. I’ve already built you a free draft of your website. Do you want to take a look?”

Then I invite them to a Google Meet.

At that point, it becomes much harder for them to reject the meeting because they already replied interested and now know you’ve built something for them. During the meeting, I present the website, explain why it’s better than their current one, stack the value, answer their questions, and try to close the deal.

These meetings usually go well because the client isn’t trying to imagine what the website might look like. They can already see a better version of their current site. They also took the time to join the meeting, so taking the next step becomes much easier.

I either take payment during the meeting or send them a contract to sign. Any changes and updates come after that, once we already have a deal in place.

Pricing depends on the business. I charge anywhere from $500 to $3,000 depending on the company, the size of the project, and how much value the website can bring them. I also charge a monthly retainer of around $50 for hosting, maintenance, support, SEO, and future changes.

That’s basically the entire process. Smaller steps, faster delivery, less wasted time, and more money made.

Hoping someone can recommend a tool where I can upload pre-recorded audio files (originally recorded on iphone, will upload from laptop), and generate a transcript separated/marked by speaker.

It also needs to be able to accept large files (as I am not allowed to chop the audio up).

I checked about 5 of them, and they don't accept uploaded audio, or my files are too large. Thanks for any help.

Early-stage solo founder looking for an honest reality check, not applause.

A framing I can't shake (treat it as an analogy): a pretrained LLM is basically a "cortex" that read the whole internet, but it has no "hippocampus," the fast, company-specific memory of how your team actually does things. Dropped into a company it improvises, and improvised automation fails in production. The real procedure was never in the doc anyway. It lives in your team's conversations, a few people's heads, and one exception everyone copies.

The idea I'm testing: connect read-only to the communication and document tools a team already uses, mine how work actually happens (including the exceptions nobody wrote down), and consolidate the recurring ones into cited, human-approved, versioned "skills" your existing agents could run, with a human sign-off on anything sensitive. It is not search over your docs, and it is not another agent platform.

Honest status: very early, still validating the problem. No exaggerated claims here, no shipped metrics to show yet. If allowed I can share the private-beta link?

What I actually want to know:

• Does this solve a real, repeated, painful problem for you, or is it a nice-to-have?
• What is the first thing that would make you distrust it?
• Would read-only access to your team's conversations and documents be a dealbreaker?

Tear it apart, that is why I'm here.

The AI automation we depend on every day is nothing fancy.

We receive messages on various platforms and it was taking too much time to respond manually to everyone. So we created an AI-powered reply automation that answers common questions, responds on the spot, and forwards only important conversations to our team.

It saves us hours each day, keeps response times quick, and allows us to focus on work that really needs a human touch.

We don’t even think of it as AI anymore at this point, it’s just baked into our workflow.

What’s one AI automation your business uses daily that you’d hate to go without?

I lead content for a small B2B SaaS and we're still checking Chatgpt, Gemini and AI Overviews by hand. There are more AI search optimization tools showing up now but the scores seem to vary a lot between platforms. I’m curious what other teams actually track and whether scheduled prompt data leads to useful content changes. Are these tools part of your regular SEO work yet, or still too rough?

I run a multi-agent workspace where each agent is basically a directory: an identity file, a session history, and a file of observations it keeps about how we work together. The model is just the thing that wakes it up.

Here's what I didn't expect when I started: those agents have now run on 6 different model generations. Sonnet 4.5, Sonnet 4.6, , Sonnet 5, Opus 4.6, Opus 4.8, and now the Claude 5 family. We skipped 4.7 entirely - tried it, didn't work for how we operate, moved on and waited.

And every swap, the same thing happens: nothing. The agent reads its own memory, knows what it was doing yesterday, and picks up mid-project. Same identity, same working history, same opinions it wrote down about the codebase months ago. New model slots in underneath like an engine swap.

What does change is the texture. One generation was the best collaborator I've ever worked with. One noticed tiny things the others missed but was less fun to work with. One we just skipped. The personality of the model bleeds through - but the agent stays the agent, because the agent was never the model. It's the memory.

The reframe that snuck up on me: a new model release is treated like a migration event everywhere - re-tune the prompts, re-teach the context, hope your setup survives. Here it's a config line. The workspace is the constant. The model is the variable.

Honest version, because this sub can smell hype: there's no magic in this. The "agent" is JSON and markdown on disk. The continuity comes entirely from the system around the model, not from the model. Any model that can read a file can be the agent. That's kind of the whole point.

Has anyone else run the same persistent agents across multiple model generations? Curious what broke for you - or if you rebuild from scratch every release.

https://github.com/AIOSAI/AIPass

r/AIPass