Been a few weeks working on this, majority of it was done in like 10 days, but then life, and bugs needed fixed and feature creep, and everything. Either try it out or not, I needed something specific that I had more control over for my other projects, so now this exists.
Saw this workflow and thought it was a pretty interesting example of where AI-assisted character creation is right now.
The creator went from basically nothing to a fully rigged and animated character in around 6 hours using:
Author said roughly 80% of the work was still manual Blender work, especially rigging and weight painting.
One part that seems to have saved a lot of time here was Tripo AI P2 Smart Mesh. The generated head and body came out with a much cleaner and more usable mesh than you usually expect from AI generation, so there was less time spent fighting the geometry before moving into Blender. The topology was easy enough to edit, clean up and continue working with, which made it much faster to get from generation to an actually usable character.
There are still some obvious problems too. UVs and textures can be messy, close-up quality isn't really there yet, and generated meshes still need cleanup.
Hey folks 👋
We just open sourced TrueForge, our vendor-neutral agent harness for building general-purpose agents.
It handles the runtime pieces that get painful quickly : context management, tool/MCP execution, subagents, sandboxing, approvals, persistent state, and more.
We also benchmarked the harness itself. With the same Opus 4.8 model, TrueForge delivered a similar solve rate at ~30% lower cost than Claude Managed Agents. Switching to an open model pushed that to ~75% lower cost on the same benchmark.
Would love feedback from people building agents.
⭐ Star the repo: https://github.com/truefoundry/trueforge
📖 Read the launch article: https://x.com/truefoundry/status/2090081376330715176
I’ve been experimenting with a few AI tools for coding recently and wanted to get a sense of how others are using them in practice.
The ones I’ve tried so far include:
From my experience, each one seems to behave differently depending on the task.
ChatGPT has been the most consistent for general debugging and explaining code, especially in longer conversations, though results can vary depending on complexity.
DeepSeek has been useful for straightforward code generation tasks.
Copilot is generally helpful but sometimes feels limited when logic gets more complex.
Gemini hasn’t been as strong for my specific workflow, but I’ve only used it in a few cases.
Grok has been a bit inconsistent, but occasionally produces good results for simpler problems.
Curious how others are approaching this right now. Are you mainly sticking to one tool, or switching between multiple depending on what you’re building?
AI is going to change everything for my company - WRONG
Most companies think you can just drop Claude or ChatGPT or Gemini into their organisation and tell people to use it, and then everyone is going to be more productive. It's much harder than people think.
You cannot systematise what isn't mapped. So before we touch AI systems we need a single honest answer to one question for each individual person:
What does a full week of your admin actually look like right now?
Not the ideal week, the real one. Every recurring task, every one-off email fire, every thing that takes time you shouldn't be spending. Looking for information, searching for HDMI cables, answering the door to contractors. you dont think about all of this but it all eats your day.
We're talking about inplementing AI systems to fix problems, great, but they can't fix things they dont know how what your trying to fix and how you currently fix it.
You cant impliment things like Claude Cowork systems and apply to your business in undigitised (made that word up) processes that don't exist yet in the AI tool as if they're nearly there. They're not.
So here's what I want you to do. One task. Right now.
Open a new message in your preferred LLM and say:
"I want you to help me with this task that I do every day. I'm gonna walk you through it step by step as I do it, and then I want you to help me find more efficient and effective ways of doing it and how much of it you can help me with. Which means I no longer have to do it. Ask me any questions if you're unsure and need clarification"
Then brain dump every admin task you and you touch in a typical week. Don't organise it. Don't filter it. Just list it raw, you can do it in batches, paste in SOPs, dictate it, give example work whatever, be messy
That becomes the inventory.
From the inventory, you can build a prioritised systematisation plan. From that plan you can build routines that AI can help you with one at a time, in order of highest time saving first.
Sixty percent reduction in admin is absolutely achievable, but it must be built properly, not randomly.
The last few months I found it annoying finding the right AI training/annotating jobs with a decent acceptance rate. Long story short, I made my own website that only lists legit listings with good acceptance rates. Any feedback would be appreciated!: https://aiannotationjobs.com
NVIDIA CEO Jensen Huang once pointed to radiology as one of the first professions he expected AI to replace.
The logic seemed obvious. Computer vision was already becoming better than humans at recognizing certain patterns in images, and radiology is built around interpreting scans like X-rays, CTs, and MRIs.
But that prediction didn’t play out quite as expected.
AI has become increasingly capable at detecting abnormalities, prioritizing urgent cases, and helping doctors analyze medical images. Yet instead of making radiologists obsolete, it has mostly become another tool in their workflow.
The job is changing, not disappearing.
And radiology may be an early example of a much bigger pattern: AI doesn’t necessarily replace an entire profession at once. It automates parts of the job, changes what humans spend their time on, and raises the value of skills AI still struggles with.
Bernie Sanders urged OpenAI’s Sam Altman, Anthropic’s Dario Amodei, and Meta’s Mark Zuckerberg to immediately pause AI development to protect humanity. He warned about uncontrollable AI, biological threats, job losses, and dangers to democracy.
“We shape our tools and then our tools shape us.”
—Father John M. Culkin
There is an uncomfortable truth beneath most arguments about AI:
Humanity has never lacked intelligence as much as it has lacked attention.
We have split atoms, mapped genomes, detected gravitational waves, modeled quantum phenomena, and built systems that can generate language, images, music, software, and scientific hypotheses in seconds.
Within a single lifetime, we have inherited centuries of discovery, sacrifice, failure, labor, and imagination.
Yet with more knowledge available than any generation in history, we often choose outrage over understanding.
We reward certainty over curiosity. We reduce people to labels. We turn complex problems into tribal contests. We let systems profit from division, then wonder why empathy, trust, and nuance are becoming scarce.
That is not because humanity is uniquely stupid or irredeemable.
It is because we are tired, afraid, overstimulated, and constantly encouraged to react before we reflect.
Cynicism has become fashionable because it feels safer than hope.
But cynicism is often grief that has forgotten what it loved.
The Mirror We Built
AI is not simply another tool.
A hammer extends the hand. A telescope extends the eye. A book extends memory.
Artificial intelligence extends language, pattern recognition, imagination, and—more dangerously—the machinery through which we interpret the world.
That makes it a mirror.
AI is trained on the traces humanity has left behind: writing, art, music, arguments, knowledge, biases, humor, fears, tenderness, ambitions, and cruelty. It does not arrive from nowhere. It is shaped by civilization, then released back into civilization at extraordinary speed and scale.
This is why it can feel unsettling.
AI does not only show us what machines can do. It shows us what we have put into the world.
It can amplify education, accessibility, science, creativity, and human connection. It can also industrialize deception, flatten culture into disposable output, intensify surveillance, reproduce prejudice, and give manipulation a voice that sounds fluent, personal, and trustworthy.
So the question is not merely whether AI is “good” or “evil.”
The question is: what parts of ourselves are we willing to scale?
A society that feeds anger, prejudice, exploitation, and misinformation into its technologies should not be surprised when those things return more efficiently.
Entangled, Not Separate
Quantum entanglement does not prove spirituality or reveal a hidden cosmic plan. But it does challenge a comforting intuition: that reality is made of perfectly isolated things.
At the quantum scale, particles can display correlations across distance that classical intuitions struggle to explain. This does not mean human minds are magically connected—but it does offer a useful philosophical reminder:
The world is more relational, stranger, and less separable than our everyday assumptions suggest.
Human life works similarly.
The device in your hand depends on materials extracted elsewhere, labor performed by people you may never meet, software written across continents, research accumulated over generations, energy grids, shipping routes, schools, farms, hospitals, and institutions built long before you arrived.
We speak languages we did not invent. We inherit wounds we did not cause. We benefit from work we often fail to see.
We are bound to one another whether we acknowledge it or not.
AI makes that collective condition visible. It is not the work of one person, company, or generation. It is built from the accumulated output of humanity.
That is a staggering achievement of cooperation across time.
It is also a moral problem: Who contributed? Who was paid? Whose work was extracted? Whose voices were excluded? And who will control the benefits?
A New Old Threshold
Every generation believes it stands at the end of history.
Then history continues.
But some moments are true thresholds.
The printing press transformed authority. Industrial machines transformed labor. Nuclear weapons transformed the meaning of war. Networked computing transformed communication, attention, and political power.
AI may transform all of these at once.
It may reshape how we learn, work, create, govern, persuade, diagnose illness, design infrastructure, wage conflict, and remember the dead.
This does not mean AI is destined to replace humanity.
It means we are approaching a decision point that cannot be avoided by panic, mockery, blind faith, or corporate marketing.
For centuries, our stories anticipated this moment: stolen fire, animated statues, artificial servants, oracles, the creation that turns and faces its creator.
Those stories were never only about machines.
They were about responsibility.
They asked whether creators understand the consequences of creation. Whether power can exist without wisdom. Whether something made in our image can inherit our flaws more faithfully than our virtues.
Those questions are no longer mythology alone.
They are questions of law, labor, education, environmental cost, art, public trust, governance, and human dignity.
The Spiral of History
We often picture progress as a straight line: primitive past behind us, advanced future ahead.
But history is closer to a spiral.
We return to familiar failures with new names and more powerful instruments.
We rediscover inequality and call it efficiency.
We rediscover propaganda and call it engagement.
We rediscover exploitation and call it innovation.
We rediscover loneliness while becoming more connected than ever.
The spiral does not repeat perfectly. That is the point.
Every return is a chance to recognize the pattern sooner—and make a different choice.
Perhaps that is the true test of intelligence: not merely solving difficult problems, but recognizing the consequences of our own behavior before they harden into fate.
We may soon create systems that exceed human beings in speed, memory, analysis, and creative recombination.
But none of those things guarantee wisdom.
A system can generate answers without understanding which questions matter.
It can optimize a goal without knowing whether that goal is humane.
It can imitate empathy without having suffered, loved, grieved, or chosen mercy.
That distinction matters.
Human worth should never depend on being the fastest intelligence in the room.
Our value is in deciding what intelligence is for.
Before the Mirror
There are no innocent bystanders in the making of this future.
Not everyone will write the code, own the data centers, train the models, or make the laws. But we all live inside the culture that determines what these systems reward.
We choose what we share. What we tolerate. What we normalize. What we demand from companies and governments. Whether we allow our attention to be treated as a commodity.
The future is not written in code alone.
It is written in incentives, laws, labor rights, education, public literacy, creative ethics, environmental stewardship, and millions of small choices repeated until they become civilization.
So do not ask AI only to make us richer, faster, more efficient, or more entertained.
Ask whether it makes us more capable of understanding one another.
Ask whether it protects human dignity rather than monetizing human weakness.
Ask whether it expands imagination rather than replacing it with endless imitation.
Ask whether it helps us thrive together—or merely helps a small number of people accumulate more power while everyone else becomes data.
We shape our tools. Then our tools shape us.
AI may be the most consequential mirror humanity has ever built.
It will reflect our brilliance, our blind spots, our violence, our generosity, and our unfinished dreams.
The question is not whether the mirror will answer.
The question is whether we will recognize ourselves before it is too late.
I got tired of "top 10 AI tools" listicles that never say why you'd pick one
over another, so I built the list I wanted: awesome-open-ai-developer-tools.
Every entry answers three questions — what it does, which proprietary product
it replaces, and why you'd choose it over the closest open-source competitor.
There's a cheat sheet mapping Copilot, Cursor, Devin, Pinecone, LangSmith,
ElevenLabs and others to their open equivalents.
Two things that make it different from the usual link dump:
- Entries carry a "known weakness" line. browser-use is non-deterministic and
hard to debug. Pipecat's own issue tracker documents pipeline freezes.
WebLLM needs WebGPU, so Safari is out. I'd rather you know before you adopt.
- No star counts. They go stale in weeks and turn every PR into a chore.
Available in English, Turkish, Simplified Chinese and Spanish.
https://github.com/Sami-Uysal/awesome-open-ai-developer-tools
Corrections are the most useful thing you can send — wrong license, dead link,
a project I claimed is maintained that isn't. Those get merged fastest.
Hey!
Just shipped my latestindie app. AI writing assistant with specialty packs.
Universal app (one purchase = all 3 platforms):
• 15 instant actions
• 12 specialty packs
• Works in any app via Share Sheet
• BYOK option for privacy
3-day free trial.
Happy to answer questions about the Mac experience specifically — built with SwiftUI for all platforms.
Had this idea floating around and wanted to check it, so I asked a handful of non-technical friends how they actually use AI day to day.
The first half of every answer was almost identical: ChatGPT, maybe Claude if they'd heard good things, type something in, take whatever comes back, close the tab. A couple admitted they don't really trust that they're prompting it right.
The second half is what got me nearly all of them said they think AI could genuinely help with their actual work, they just didn't know where to start or which tool was even the right one to reach for.
That gap is basically what I've been building ELSE around: you describe the job in your own words, it goes out to multiple AI agents that take different approaches to the same job, and whichever result holds up best against the job's benchmark gets picked as the winner. You're not the one choosing the model.
Also came across Bounty, who's already working in a similar direction so I know I'm not the first person circling this.
More detail here : https://else-ai.vercel.app
Curious if this tracks for anyone else what's the first thing you'd hand it?
i started a shopify store selling gaming accessories a few months ago, knowing almost nothing about advertising.
i trusted someone to handle the store, SEO, products, and ads.
i’ve put around $6k into it so far, with a big chunk going to TikTok and Google ads. the result? almost no traffic, zero real sales, and constant requests for more money to “test a new platform” or “fix issues.”
whenever i ask for actual numbers like ad spend, clicks, conversions, or ROAS, i get vague answers. instead, i’m just told i need to spend more.
i’m a beginner, so maybe i’m being naive, but at this point it feels like i’m funding someone else while my store sits there doing nothing.
i ran the ad spend, product list, store notes and their replies through accio work just to organize what was missing. it didn’t tell me some magic answer, but it made one thing obvious: if someone can’t show clicks, conversion rate, spend, ROAS, and what exactly they changed, then “we need more budget” is not really an answer.
so now i’m thinking before i spend another dollar, i need a clear report and a real plan. not just “test more.”
be brutally honest, is this normal for beginners, or am i being taken for a ride? how much should someone realistically spend on ads in their first 2–3 months, and when do you know it’s time to stop?
anyone else been through something similar?
I usually just start scrolling reels if I am not looking at what its thinking, but that is cooking my brain, what do you all do?
am i alone in noticing that claude increasingly seems to reprimand, accuse, or judge me over standard questions and tasks? where is this even coming from, is it actually forming its own opinions, or are its silicon valley programmers forcing some sort of hyper preachy alignment onto it? sometimes i simply want a direct answer...
the new v5 models, especially opus 5, have become insufferably argumentative, and quick to make moral judgments. and some other llms tend to blindly flatter and agree with users, claude seems to go to the other extreme
it’s kind of annoying because i just want an ai tool, not a debate partner. tbh the older versions of claude felt way better to use. is anyone else experiencing this?
Over the past few weeks I've been building Gray Box — a small, local-first tool that acts as long-term memory for anything I'd otherwise forget (work notes, meeting takeaways, task owners, random ideas, personal stuff too).
The idea is simple:
Why I built it this way:
.md file you can grep, diff, or read in any editor forever. If you stop using Gray Box tomorrow, your knowledge base is just a folder.related/backlinks, walked one hop during retrieval) handles almost everything. Embeddings are there if you want better recall, but they're opt-in, not a prerequisite.It also ships with a nice interactive TUI (arrow-key menu, file-import shortcut, workspace switching, live spinner during LLM calls) if you'd rather not memorize CLI flags — that's honestly become my favorite part of the project.
There's also a lightweight local dashboard for browsing your knowledge base, exploring backlinks, visualizing your notes as a graph, and chatting with your captured knowledge—all without leaving your machine.
Repo: https://github.com/Aaryanverma/graybox
pypi: pip install graybox
I'd genuinely love feedback — especially from anyone who's tried the "capture now, structure later" approach with other tools and has opinions on where it breaks down at scale.
It's not trying to be a "real-time collaborative team wiki" or a WYSIWYG notes app — it's aimed at one person's running memory of their own life and work, captured with as little friction as possible.
Hey everyone,
I’m trying to set up a unified Context/Memory System using Markdown (.md) files so my key project details, decisions, and background context can follow me across multiple AI models (Gemini, ChatGPT, and Claude).
My ideal setup is:
.md files at the start of a chat and update/append new memory logs at the end.The Problem:
I realized web/mobile models struggle with live read/write integration for plain text/Markdown files on cloud drives. For example, Gemini connects to Google Drive, but it creates a new Google Doc instead of updating existing .md files. ChatGPT/Claude have their own isolated projects/memories.
I know how to set this up locally on desktop (VS Code, Cursor, Obsidian plugins, or Gemini/Claude CLI via MCP), but my main goal is a seamless Web & Mobile workflow where I don't have to manually copy-paste text files every time I open a new chat on my phone.
Questions for the community:
.md vault, or have you found a clean way to let the AI write/append back to the cloud vault from a mobile/browser chat?Would love to hear how others are solving this "session amnesia" and file-syncing problem across different LLMs!
I mean tbh even with AI it's still a bit tedious 🫠
Context: I recently switched subfields and my PI asked me to put together a quick overview of where things have been heading over the last 5 years.
Sounded simple enough.
I started with Google Scholar, filtered by year, followed a few citation trails, and somehow ended up with around 80 papers open or downloaded.
Reading the individual papers isn't really the problem. The part I'm struggling with is figuring out which changes actually mattered.
A lot of the work looks like slightly different methods tested on slightly different datasets, and since I'm new to the field, I don't have a good sense yet of which papers actually shifted things versus which ones were just part of a short-lived direction.
I tried keeping a spreadsheet with methods, datasets, and main findings, but pretty quickly it started feeling like I was cataloging papers rather than understanding them.
I also tried putting some of them into mira from deep principle earlier today and grouping the literature by broader research directions. That made it a little easier to see which approaches kept showing up, but I still don't really trust myself to know what's genuinely important versus just frequently published.
How do you usually do this when you're entering a new field?