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I’ve been testing Qoder, and the biggest difference I noticed is that it feels less like asking an AI to write code and more like handing an AI agent an actual task. I started from Qoder’s new task screen, described the outcome I wanted, selected my workspace, model, and...

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Ethan Cole AI

121,823 subscribers

251,390 просмотров • 9 дней назад •via X (Twitter)

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Комментарии: 19

Фото профиля Ethan Cole AI
Ethan Cole AI9 дней назад

Try Qoder free with 600 Credits 👇 300 from the Pro trial + 300 with my code C46T6F.

Фото профиля NeuraFlow AI
NeuraFlow AI8 дней назад

Qwen3.8-Flash at 0x credits is definitely worth testing before September 30

Фото профиля Lacoste AI | Tools
Lacoste AI | Tools8 дней назад

Agentic coding tools are getting seriously impressive lately

Фото профиля Olivia Reed
Olivia Reed8 дней назад

600 credits for new users is actually pretty generous

Фото профиля Erina | AI Tools & News
Erina | AI Tools & News9 дней назад

This is the shift: stop prompting for code, start assigning work.

Фото профиля Md. Robius Sany
Md. Robius Sany9 дней назад

Awesome share

Фото профиля David
David9 дней назад

Excellent share thanks

Фото профиля COMMON AI
COMMON AI9 дней назад

Excellent Share

Фото профиля Antonio Costa | IA
Antonio Costa | IA8 дней назад

This feels much closer to delegating work than just prompting an AI

Фото профиля Chloe Wells
Chloe Wells9 дней назад

Thanks it’s really nice share

Фото профиля Arcane Matrix | AI
Arcane Matrix | AI9 дней назад

Qoder is insane

Фото профиля Ai With Piyas
Ai With Piyas9 дней назад

Excellent share

Фото профиля Onil Coder
Onil Coder9 дней назад

Appreciate you sharing this.

Фото профиля Theo Builds AI
Theo Builds AI8 дней назад

The ability to pause, redirect, or take over is exactly what these coding agents need

Фото профиля Nova IA
Nova IA8 дней назад

Being able to watch the agent work and step in when needed is a big plus

Фото профиля Justin Brave💡
Justin Brave💡9 дней назад

Impressive work. Thanks for sharing your perspective.

Фото профиля SynapseOps AI
SynapseOps AI8 дней назад

Seeing the planning and execution process makes it way easier to trust the result

Фото профиля rockcovenant
rockcovenant8 дней назад

Do my dzs

Фото профиля Alex Carter AI
Alex Carter AI8 дней назад

The verification step at the end is probably the part I like most

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I gave Qoder a task instead of a prompt. And it actually did the work. It read the context, made a plan, used the tools it needed, executed the task, and verified the result before finishing. That’s the big difference with Qoder. It feels less like chatting with AI and more like handing a task to an AI agent on your computer. You describe the outcome you want. Qoder can then: • Read your workspace and context • Plan how to complete the task • Execute commands and make file changes • Use the browser or desktop when needed • Let you pause, adjust, or take over • Verify the result when it’s done And right now, Qwen3.8-Flash runs at 0.0x Credits through September 30. New users can also get 600 Credits in total. Try Qoder here: If you already use AI for coding or everyday computer work, this is worth experimenting with.
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I gave Qoder a task instead of a prompt. And it actually did the work. It read the context, made a plan, used the tools it needed, executed the task, and verified the result before finishing. That’s the big difference with Qoder. It feels less like chatting with AI and more like handing a task to an AI agent on your computer. You describe the outcome you want. Qoder can then: • Read your workspace and context • Plan how to complete the task • Execute commands and make file changes • Use the browser or desktop when needed • Let you pause, adjust, or take over • Verify the result when it’s done And right now, Qwen3.8-Flash runs at 0.0x Credits through September 30. New users can also get 600 Credits in total. Try Qoder here: If you already use AI for coding or everyday computer work, this is worth experimenting with.

Shushant Lakhyani

143,425 просмотров • 9 дней назад

I wanted to see if an AI agent could take one goal all the way to a finished artifact. I gave Qoder a weekly competitor-tracking task. I started on the task screen, selected my workspace, left the model on Auto, set permissions, and sent the outcome instead of a step-by-step prompt. Qoder read the context and began planning. Rather than one generic research pass, it ran subagents for Cursor, Windsurf and GitHub Copilot in parallel. I watched them check public updates and bring their findings back into the main task. The work was visible: planning, tool calls, terminal commands, file changes and progress updates. It did not feel like another chat answer. It felt like watching a task move through an actual workflow. The final artifact was competitor-weekly-2026-09-22.md It summarized the week's highlights and included sources and caveats, including where public evidence was limited. That made the output reviewable: I could check the sources, question the conclusions, and decide what to do next. That is the part I care about. Qoder is a desktop AI agent you can hand an outcome as a task. It reads context, plans, uses tools, executes and verifies. You can watch, adjust, pause or take over. One goal → planning → parallel research → terminal work → verified Markdown report. In the model picker, Qwen3.8-Flash was showing 0.0x Credits through September 30, so that option was available at zero cost during the offer. If you want to try it, Qoder has a 14-day Pro trial with 300 Credits. Sign up first using this link at before you download. That adds another 300 Credits, for 600 total.
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I wanted to see if an AI agent could take one goal all the way to a finished artifact. I gave Qoder a weekly competitor-tracking task. I started on the task screen, selected my workspace, left the model on Auto, set permissions, and sent the outcome instead of a step-by-step prompt. Qoder read the context and began planning. Rather than one generic research pass, it ran subagents for Cursor, Windsurf and GitHub Copilot in parallel. I watched them check public updates and bring their findings back into the main task. The work was visible: planning, tool calls, terminal commands, file changes and progress updates. It did not feel like another chat answer. It felt like watching a task move through an actual workflow. The final artifact was competitor-weekly-2026-09-22.md It summarized the week's highlights and included sources and caveats, including where public evidence was limited. That made the output reviewable: I could check the sources, question the conclusions, and decide what to do next. That is the part I care about. Qoder is a desktop AI agent you can hand an outcome as a task. It reads context, plans, uses tools, executes and verifies. You can watch, adjust, pause or take over. One goal → planning → parallel research → terminal work → verified Markdown report. In the model picker, Qwen3.8-Flash was showing 0.0x Credits through September 30, so that option was available at zero cost during the offer. If you want to try it, Qoder has a 14-day Pro trial with 300 Credits. Sign up first using this link at before you download. That adds another 300 Credits, for 600 total.

Nelly;

159,865 просмотров • 13 дней назад

I stopped doing my weekly competitor research manually. I gave Qoder one recurring task: → Track Cursor, Windsurf & GitHub Copilot → Check official updates, X, Reddit & Product Hunt → Find the week’s important releases, news & partnerships → Turn it all into a structured report and save it automatically The best part? I set the workflow once. Qoder handles the research → analysis → report. That’s the kind of AI workflow I actually want to automate. Qwen 3.8 Flash is currently at 0.0x Credits through September 30. Enter my code 9797YB at first, then download Qoder and sign in. You will get 600 Credits: 300 from the trial and 300 from my code.
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I stopped doing my weekly competitor research manually. I gave Qoder one recurring task: → Track Cursor, Windsurf & GitHub Copilot → Check official updates, X, Reddit & Product Hunt → Find the week’s important releases, news & partnerships → Turn it all into a structured report and save it automatically The best part? I set the workflow once. Qoder handles the research → analysis → report. That’s the kind of AI workflow I actually want to automate. Qwen 3.8 Flash is currently at 0.0x Credits through September 30. Enter my code 9797YB at first, then download Qoder and sign in. You will get 600 Credits: 300 from the trial and 300 from my code.

Kalsoom (ghotai )

118,728 просмотров • 10 дней назад

Created a new task with Qoder: gave it one of my own talk recordings and its transcript, and asked it to find the five most clippable moments, caption them, and cut them with ffmpeg. This is the whole run (check the video 😏) • Qoder is a desktop app where you hand work over as a task rather than chatting about it. • You describe the outcome, pick a model, set permissions, and then watch it plan, run commands, and touch files. You can pause it or take over at any point. • The model picker is worth a look on its own. You choose per task or leave it on Auto, and Qwen3.8-Flash is running at 0.0x through September 30, so you can work through real tasks at zero Credits right now. 🪙 14-day Pro trial gets you 300 Credits. My code gets you another 300. 🔗 Enter AD23XU at first, then download and sign in. [1/2] ↓ #ad
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Created a new task with Qoder: gave it one of my own talk recordings and its transcript, and asked it to find the five most clippable moments, caption them, and cut them with ffmpeg. This is the whole run (check the video 😏) • Qoder is a desktop app where you hand work over as a task rather than chatting about it. • You describe the outcome, pick a model, set permissions, and then watch it plan, run commands, and touch files. You can pause it or take over at any point. • The model picker is worth a look on its own. You choose per task or leave it on Auto, and Qwen3.8-Flash is running at 0.0x through September 30, so you can work through real tasks at zero Credits right now. 🪙 14-day Pro trial gets you 300 Credits. My code gets you another 300. 🔗 Enter AD23XU at first, then download and sign in. [1/2] ↓ #ad

Eleftheria Batsou

148,355 просмотров • 11 дней назад

You don't need to know how to code to turn a messy spreadsheet into something you can actually use. I had an Excel file of 20 brands I want to pitch. I dropped it in a folder, opened Qoder, and typed one sentence: turn this into a dashboard with search. Then I watched. It read the file, made a plan, wrote the code, opened the page in the browser, and tested the search on its own. I added a /goal so it knew when it was done: every brand shows, search works. The model: Qwen3.8-Flash, free through September 30. Zero Credits for this whole run. Want to try it? Enter my code BPU7C6 at first, then download Qoder and sign in. You will get 600 Credits: 300 from the trial and 300 from my code.
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You don't need to know how to code to turn a messy spreadsheet into something you can actually use. I had an Excel file of 20 brands I want to pitch. I dropped it in a folder, opened Qoder, and typed one sentence: turn this into a dashboard with search. Then I watched. It read the file, made a plan, wrote the code, opened the page in the browser, and tested the search on its own. I added a /goal so it knew when it was done: every brand shows, search works. The model: Qwen3.8-Flash, free through September 30. Zero Credits for this whole run. Want to try it? Enter my code BPU7C6 at first, then download Qoder and sign in. You will get 600 Credits: 300 from the trial and 300 from my code.

Jay

143,413 просмотров • 11 дней назад

Competitor tracking is one of those tasks that's easy to skip because it's slow. I tested automating it with Qoder. The goal was simple: every week, research what changed across competitors and produce a report I could actually review. Qoder planned the research, checked public updates, ran tasks in parallel, and compiled everything into a structured Markdown report with sources and caveats where evidence was limited. The workflow went from manual to: schedule → research → verify → analyze → report. I also ran Qwen3.8-Flash on it, which is showing 0.0x Credits through September 30. Qoder has a 14-day Pro trial with 300 Credits. Sign up before downloading through this link and you get another 300 — 600 Credits total: The interesting part isn't just the research. It's having an AI agent turn a recurring task into a finished artifact automatically.
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Competitor tracking is one of those tasks that's easy to skip because it's slow. I tested automating it with Qoder. The goal was simple: every week, research what changed across competitors and produce a report I could actually review. Qoder planned the research, checked public updates, ran tasks in parallel, and compiled everything into a structured Markdown report with sources and caveats where evidence was limited. The workflow went from manual to: schedule → research → verify → analyze → report. I also ran Qwen3.8-Flash on it, which is showing 0.0x Credits through September 30. Qoder has a 14-day Pro trial with 300 Credits. Sign up before downloading through this link and you get another 300 — 600 Credits total: The interesting part isn't just the research. It's having an AI agent turn a recurring task into a finished artifact automatically.

Shruti

142,345 просмотров • 12 дней назад

Your Downloads folder is probably a mess. Mine was. I opened Qoder, picked the folder, and typed one sentence: sort everything by type, move only, never delete. It looked through the files, made a plan, asked me before each move, then wrote a report of what went where. I set a /goal so it knew when it was done: no loose files, nothing deleted. Qwen3.8-Flash is free through September 30, so this cost me zero Credits. Try it: Enter my code AKMSNL at first, then download Qoder and sign in. You will get 600 Credits: 300 from the trial and 300 from my code.
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Your Downloads folder is probably a mess. Mine was. I opened Qoder, picked the folder, and typed one sentence: sort everything by type, move only, never delete. It looked through the files, made a plan, asked me before each move, then wrote a report of what went where. I set a /goal so it knew when it was done: no loose files, nothing deleted. Qwen3.8-Flash is free through September 30, so this cost me zero Credits. Try it: Enter my code AKMSNL at first, then download Qoder and sign in. You will get 600 Credits: 300 from the trial and 300 from my code.

Alex Moore

132,769 просмотров • 10 дней назад

I wanted to see if an AI agent could take one business task all the way to a verified artifact, so I gave Qoder a real one. In General mode, I selected the Business Data Analysis workspace, chose Qwen3.8-Flash from the model picker, enabled full access, and asked it to analyze every file in the folder and build an executive performance report for Jul 1-Sep 20, 2026. The brief included KPIs, revenue, gross profit, margins, monthly trends, regional and product performance, target attainment, marketing efficiency, customer mix, weighted CRM pipeline, anomalies, and charts. Qoder planned the task, read the source files, and ran terminal commands. It hit real issues: a broken NumPy environment, old folder paths, inaccurate claims, and chart label collisions. It switched to a working Python stack, repaired the scripts, corrected a "2x October target" claim to September, fixed a "2:1" product-tier claim that was actually 1.5:1, and re-ran the validate -> metrics -> charts -> report pipeline. The Browser Use step was the proof: Qoder opened the generated HTML, captured screenshots, and checked that the charts rendered correctly. The final report showed $450,635 net revenue, $311,262 gross profit, 69.1% gross margin, 937 orders, and $1.101M weighted pipeline from 120 open opportunities. That is the difference between a chat answer and an agent workflow: I could inspect the actions, challenge the output, and verify the result. Qwen3.8-Flash was at 0.0x Credits through Sep 30. Sign up first using this link at before you download. You will get 600 Credits: 300 from the trial and 300 from my code.
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I wanted to see if an AI agent could take one business task all the way to a verified artifact, so I gave Qoder a real one. In General mode, I selected the Business Data Analysis workspace, chose Qwen3.8-Flash from the model picker, enabled full access, and asked it to analyze every file in the folder and build an executive performance report for Jul 1-Sep 20, 2026. The brief included KPIs, revenue, gross profit, margins, monthly trends, regional and product performance, target attainment, marketing efficiency, customer mix, weighted CRM pipeline, anomalies, and charts. Qoder planned the task, read the source files, and ran terminal commands. It hit real issues: a broken NumPy environment, old folder paths, inaccurate claims, and chart label collisions. It switched to a working Python stack, repaired the scripts, corrected a "2x October target" claim to September, fixed a "2:1" product-tier claim that was actually 1.5:1, and re-ran the validate -> metrics -> charts -> report pipeline. The Browser Use step was the proof: Qoder opened the generated HTML, captured screenshots, and checked that the charts rendered correctly. The final report showed $450,635 net revenue, $311,262 gross profit, 69.1% gross margin, 937 orders, and $1.101M weighted pipeline from 120 open opportunities. That is the difference between a chat answer and an agent workflow: I could inspect the actions, challenge the output, and verify the result. Qwen3.8-Flash was at 0.0x Credits through Sep 30. Sign up first using this link at before you download. You will get 600 Credits: 300 from the trial and 300 from my code.

JAYDEN™

158,041 просмотров • 9 дней назад

I wanted to see if Qoder could turn a real World Development Indicators CSV into something verifiable, not just a pretty summary. I opened a new task, selected the QoderCSVReport workspace, left the model on Auto, set permissions, and gave Qoder the CSV. The file had 1,045 rows, 14 columns, and ten years of data from 2016 to 2025. Qoder validated the file, treated .. as null instead of zero, transformed the wide table into a country-year-series dataset, and measured coverage and missing rates. It then generated summary.json, report.md, dashboard.html, README.md, and a verification script. The verifier independently re-read the CSV and checked the totals, country count, Series count, yearly missing rates, and the highest and lowest values against the report and dashboard. That last step is what made it useful. The output was not just a chat answer. It was a real artifact I could inspect and verify. The dashboard includes KPI cards, yearly missing-rate charts, Series coverage rankings, country coverage, and filters for Country and Series. It is responsive on desktop and mobile and uses no external CDN assets. The generated files are all in English. I used Auto for this run. Qwen3.8-Flash was available in the model picker at 0.0x Credits through September 30, so that option was free during the offer. Try Qoder → You’ll get 600 Credits total: 300 from the trial + 300 from my code.
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I wanted to see if Qoder could turn a real World Development Indicators CSV into something verifiable, not just a pretty summary. I opened a new task, selected the QoderCSVReport workspace, left the model on Auto, set permissions, and gave Qoder the CSV. The file had 1,045 rows, 14 columns, and ten years of data from 2016 to 2025. Qoder validated the file, treated .. as null instead of zero, transformed the wide table into a country-year-series dataset, and measured coverage and missing rates. It then generated summary.json, report.md, dashboard.html, README.md, and a verification script. The verifier independently re-read the CSV and checked the totals, country count, Series count, yearly missing rates, and the highest and lowest values against the report and dashboard. That last step is what made it useful. The output was not just a chat answer. It was a real artifact I could inspect and verify. The dashboard includes KPI cards, yearly missing-rate charts, Series coverage rankings, country coverage, and filters for Country and Series. It is responsive on desktop and mobile and uses no external CDN assets. The generated files are all in English. I used Auto for this run. Qwen3.8-Flash was available in the model picker at 0.0x Credits through September 30, so that option was free during the offer. Try Qoder → You’ll get 600 Credits total: 300 from the trial + 300 from my code.

AI StackLab

188,759 просмотров • 13 дней назад

AI coding tools are great at generating code. But can an AI agent take a vague idea, understand the whole project, make decisions, write the code, run it, test it, and actually turn it into something usable? I put EvoX Agent through a real-world task to find out. Here’s what happened ↓ #EvoxAgent #EvoMap #AlAgent
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AI coding tools are great at generating code. But can an AI agent take a vague idea, understand the whole project, make decisions, write the code, run it, test it, and actually turn it into something usable? I put EvoX Agent through a real-world task to find out. Here’s what happened ↓ #EvoxAgent #EvoMap #AlAgent

Jay Bisen

71,887 просмотров • 24 дней назад

“Atria Dawn Preview” just caught my attention after I tested it on a real frontend task. I tested Atria ASI with Claude Code on a real frontend task: build a modern, responsive landing page using HTML, CSS, and JavaScript. It created the files, validated the code, and opened the final result for review. What stood out was the workflow. It went from understanding the task to writing code, checking the result, and delivering something I could actually run and verify. That makes “Atria Dawn Preview” interesting to me for research, development, and other professional workflows where the goal isn’t just an answer, but a usable and verifiable result. Definitely an agent I’ll be keeping an eye on. Official links:👇 API: Website: Looking forward to seeing where Atria Dawn Preview goes next.
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“Atria Dawn Preview” just caught my attention after I tested it on a real frontend task. I tested Atria ASI with Claude Code on a real frontend task: build a modern, responsive landing page using HTML, CSS, and JavaScript. It created the files, validated the code, and opened the final result for review. What stood out was the workflow. It went from understanding the task to writing code, checking the result, and delivering something I could actually run and verify. That makes “Atria Dawn Preview” interesting to me for research, development, and other professional workflows where the goal isn’t just an answer, but a usable and verifiable result. Definitely an agent I’ll be keeping an eye on. Official links:👇 API: Website: Looking forward to seeing where Atria Dawn Preview goes next.

Aryan Rakib

35,129 просмотров • 21 дней назад

There’s a pattern with agent demos. You see the setup, then the result. The messy middle is usually missing. Nex-N2.5 Pro caught my attention because it leaves that part in. You can watch it read the screen, act, see what changed, and adjust. That’s where the 397B multimodal build comes in. Take a look at the Blender task below. Instead of just generating some code and calling it a day, it actually works through the whole thing. here’s what I noticed: It started by creating the scene, then checked the result instead of assuming everything went right. Something looked off? It went back in, changed the setup, rendered again and checked the new result. That little loop is what stood out to me. Because real computer work rarely goes: prompt → perfect result. There’s usually a lot of: “this doesn’t look right, let me fix that.” Seeing an AI handle that part inside Blender is probably the most interesting thing about this demo. You can try Nex-N2.5 Pro for free on OpenRouter.
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There’s a pattern with agent demos. You see the setup, then the result. The messy middle is usually missing. Nex-N2.5 Pro caught my attention because it leaves that part in. You can watch it read the screen, act, see what changed, and adjust. That’s where the 397B multimodal build comes in. Take a look at the Blender task below. Instead of just generating some code and calling it a day, it actually works through the whole thing. here’s what I noticed: It started by creating the scene, then checked the result instead of assuming everything went right. Something looked off? It went back in, changed the setup, rendered again and checked the new result. That little loop is what stood out to me. Because real computer work rarely goes: prompt → perfect result. There’s usually a lot of: “this doesn’t look right, let me fix that.” Seeing an AI handle that part inside Blender is probably the most interesting thing about this demo. You can try Nex-N2.5 Pro for free on OpenRouter.

SayoClips

165,376 просмотров • 25 дней назад

AI agents are moving past the point where generating an answer is enough. The real question is whether an agent can stay inside a real environment long enough to actually finish the job. That’s what makes Nex-N2.5 interesting to me It is built around long-horizon computer use, with agents that can operate browsers and software, execute programs, see the results through visual feedback, and keep correcting their work instead of stopping at the first output. Nex-N2.5 Pro was put through exactly that kind of workflow: build a SQL interpreter, write tests, run it, identify what was missing, implement the next pieces, and keep iterating. The model progressively handled things like WHERE, JOIN, subqueries, qualified names and table aliases while testing its own implementation along the way. That is the difference I care about with agentic AI. The agent isn't just producing an artifact. It's operating inside a feedback loop: write → run → inspect the result → fix the problem → verify again. Nex-N2.5 is also released as a family of Mini, Pro and Max models, giving developers different options depending on the task and deployment requirements. The Nex-N2.5 Mini and Pro are both available through OpenRouter for free for a limited time: Try it out here: The real shift isn't just better AI outputs. It’s agents that can interact with an environment, respond to what happens, and keep working until the task is actually finished.
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AI agents are moving past the point where generating an answer is enough. The real question is whether an agent can stay inside a real environment long enough to actually finish the job. That’s what makes Nex-N2.5 interesting to me It is built around long-horizon computer use, with agents that can operate browsers and software, execute programs, see the results through visual feedback, and keep correcting their work instead of stopping at the first output. Nex-N2.5 Pro was put through exactly that kind of workflow: build a SQL interpreter, write tests, run it, identify what was missing, implement the next pieces, and keep iterating. The model progressively handled things like WHERE, JOIN, subqueries, qualified names and table aliases while testing its own implementation along the way. That is the difference I care about with agentic AI. The agent isn't just producing an artifact. It's operating inside a feedback loop: write → run → inspect the result → fix the problem → verify again. Nex-N2.5 is also released as a family of Mini, Pro and Max models, giving developers different options depending on the task and deployment requirements. The Nex-N2.5 Mini and Pro are both available through OpenRouter for free for a limited time: Try it out here: The real shift isn't just better AI outputs. It’s agents that can interact with an environment, respond to what happens, and keep working until the task is actually finished.

Mide

20,759 просмотров • 25 дней назад

I gave an AI one instruction: “Build me a playable 3D game from scratch.” Then I let it cook. 👀 I tested Tencent Hy4 preview inside WorkBuddy, and it went way beyond just generating a few snippets of code. It could: → Plan the game structure → Generate the code across files → Create the UI and interactions → Debug problems along the way → Iterate on the project while keeping context That last part is what caught my attention. Hy4 preview is built for these long-horizon tasks, with 770B total parameters, 49B active parameters, and a 1M-token context window. It’s designed for coding agents, game development, tool use, research, and other workflows where the model has to actually do the work, not just answer a prompt. And right now, you can try Hy4 preview for free on WorkBuddy until September 10. If you want to see how far an AI agent can take a real project, try it yourself: See what you can build with it. Tencent AI
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I gave an AI one instruction: “Build me a playable 3D game from scratch.” Then I let it cook. 👀 I tested Tencent Hy4 preview inside WorkBuddy, and it went way beyond just generating a few snippets of code. It could: → Plan the game structure → Generate the code across files → Create the UI and interactions → Debug problems along the way → Iterate on the project while keeping context That last part is what caught my attention. Hy4 preview is built for these long-horizon tasks, with 770B total parameters, 49B active parameters, and a 1M-token context window. It’s designed for coding agents, game development, tool use, research, and other workflows where the model has to actually do the work, not just answer a prompt. And right now, you can try Hy4 preview for free on WorkBuddy until September 10. If you want to see how far an AI agent can take a real project, try it yourself: See what you can build with it. Tencent AI

Toha Khan

66,214 просмотров • 1 месяц назад

I gave Hermes OS one voice command and watched it build a website “Create a website for a hair salon.” My agent started writing code, moving through windows, and building the site right in front of me. I could follow the whole process in real time I just added native voice commands to Hermes OS, and there’s an important difference: Controlling the OS uses zero model tokens. Those commands run natively. You can control your entire computer through your voice Tokens only come into play when I ask Hermes Agent to do a task This is what I wanted Hermes OS to feel like Tell my computer what I need. Watch it get to work.
2:09

I gave Hermes OS one voice command and watched it build a website “Create a website for a hair salon.” My agent started writing code, moving through windows, and building the site right in front of me. I could follow the whole process in real time I just added native voice commands to Hermes OS, and there’s an important difference: Controlling the OS uses zero model tokens. Those commands run natively. You can control your entire computer through your voice Tokens only come into play when I ask Hermes Agent to do a task This is what I wanted Hermes OS to feel like Tell my computer what I need. Watch it get to work.

Luke The Dev

20,122 просмотров • 10 дней назад

A lot of AI agent products promise “automation.” What they actually give you is another dashboard to micromanage. You spend hours wiring prompts, APIs, workflows, memory systems, and tool calls before the agents even begin doing real work. That’s been my experience with most agent stacks so far. Then I tried Helio The difference hit immediately. Instead of configuring everything like an engineer, I just gave the system an outcome. The AI teammates coordinated tasks, delegated work between themselves, and started executing while I was away. No babysitting. No endless tweaking. No orchestration headaches. For the first time, an AI agent product felt less like “using software” and more like hiring a small autonomous team. That’s the direction the market is moving toward fast.
0:39

A lot of AI agent products promise “automation.” What they actually give you is another dashboard to micromanage. You spend hours wiring prompts, APIs, workflows, memory systems, and tool calls before the agents even begin doing real work. That’s been my experience with most agent stacks so far. Then I tried Helio The difference hit immediately. Instead of configuring everything like an engineer, I just gave the system an outcome. The AI teammates coordinated tasks, delegated work between themselves, and started executing while I was away. No babysitting. No endless tweaking. No orchestration headaches. For the first time, an AI agent product felt less like “using software” and more like hiring a small autonomous team. That’s the direction the market is moving toward fast.

Atul Kumar

34,966 просмотров • 4 месяцев назад

The most interesting part of Hedra Agent 2 isn't pressing “generate.” It’s the back-and-forth before that. For this project, the Agent planned the concept, asked me to clarify the creative direction, adapted the workflow based on my answer, and then carried it through toward the finished media… all inside the same Space. This is what an actual AI creative workflow should feel like. Explore Agent 2:
0:50

The most interesting part of Hedra Agent 2 isn't pressing “generate.” It’s the back-and-forth before that. For this project, the Agent planned the concept, asked me to clarify the creative direction, adapted the workflow based on my answer, and then carried it through toward the finished media… all inside the same Space. This is what an actual AI creative workflow should feel like. Explore Agent 2:

SONIA

111,322 просмотров • 1 месяц назад

I gave an AI one simple challenge: “Take my product and turn it into a launch video.” No editing timeline. No complicated production brief. No jumping between different tools. I gave Pexo product link, explained what the product does, who it’s for, and what I wanted the launch to feel like. Then I just started talking through the creative. The interesting part was being able to iterate through conversation. Change the pacing. Adjust the messaging. Try a different visual direction. Refine the final result. Instead of treating video creation like a one-shot prompt, Pexo made it feel like an ongoing creative process. And it brings the pieces together visuals, voice, subtitles, motion, and graphics to build the final video. That changed the way I think about AI video. The prompt gets you started. The conversation gets you to the final cut. If you have a product you’re launching, give Pexo your URL and see what it can create. I’ve secured an exclusive perk from Pexo for you! After signing up, open your profile menu, select ‘Redeem code,’ and enter JRNZT8 to claim 100 trial credits. This limited-time offer is available to the first 50 people only, so grab yours early 👀 Try Pexo #MadeWithPexo
1:34

I gave an AI one simple challenge: “Take my product and turn it into a launch video.” No editing timeline. No complicated production brief. No jumping between different tools. I gave Pexo product link, explained what the product does, who it’s for, and what I wanted the launch to feel like. Then I just started talking through the creative. The interesting part was being able to iterate through conversation. Change the pacing. Adjust the messaging. Try a different visual direction. Refine the final result. Instead of treating video creation like a one-shot prompt, Pexo made it feel like an ongoing creative process. And it brings the pieces together visuals, voice, subtitles, motion, and graphics to build the final video. That changed the way I think about AI video. The prompt gets you started. The conversation gets you to the final cut. If you have a product you’re launching, give Pexo your URL and see what it can create. I’ve secured an exclusive perk from Pexo for you! After signing up, open your profile menu, select ‘Redeem code,’ and enter JRNZT8 to claim 100 trial credits. This limited-time offer is available to the first 50 people only, so grab yours early 👀 Try Pexo #MadeWithPexo

Mohini Shewale

39,652 просмотров • 14 дней назад

Yesterday I posted a clip of an alleged response from ChatGPT. I stand by that post despite some push back as it aligned with my reading of the bill and the fact that I had run through. 3 AIs. I am still working on cross referencing the outputs from the AIs alongside my notes from the bill. What I did not do is run it through Grok. So this clip is an interesting clip. Once again it aligns with both my notes and also my post from yesterday. 🎥 TikTok -
7:17

Yesterday I posted a clip of an alleged response from ChatGPT. I stand by that post despite some push back as it aligned with my reading of the bill and the fact that I had run through. 3 AIs. I am still working on cross referencing the outputs from the AIs alongside my notes from the bill. What I did not do is run it through Grok. So this clip is an interesting clip. Once again it aligns with both my notes and also my post from yesterday. 🎥 TikTok -

𝔗𝔯𝔲𝔱𝔥 𝔐𝔞𝔱𝔱𝔢𝔯𝔰

25,396 просмотров • 1 год назад

I cant believe this guy just made a permanent solution to context bloat and open sourced it all! when we tested this tool (Context+) for solving an issue on the OpenCode repository, the agent using this tool used ~6.5k fewer tokens, found the code and fixed it in half the time! the results were surprising: 6 to 10k tokens saved per prompt, completed task in ~2 minutes while the agent running without the tool took ~4 mins for the same and got stuck in loops bro built an entire beast by using all the modern tools that we could think of: undo trees, semantic search by meaning (by haskellforall), advanced refactoring, blast radius, advanced file context trees, restore points... i can keep going on semantic code search and context trees are the future of agentic coding and this tool proves it the feature i loved the most is semantic search and how it gets things done 2x faster with least possible tokens it makes an agent that actually knows what it’s doing and not just guessing, it makes meaning from your code similar to RAG. if you aren't optimizing your context, you are just burning money the developer says this tool is still under development, it can have unexpected behavior and the docs need updates but the video shows the reality of how fast it can be github: get here:
0:46

I cant believe this guy just made a permanent solution to context bloat and open sourced it all! when we tested this tool (Context+) for solving an issue on the OpenCode repository, the agent using this tool used ~6.5k fewer tokens, found the code and fixed it in half the time! the results were surprising: 6 to 10k tokens saved per prompt, completed task in ~2 minutes while the agent running without the tool took ~4 mins for the same and got stuck in loops bro built an entire beast by using all the modern tools that we could think of: undo trees, semantic search by meaning (by haskellforall), advanced refactoring, blast radius, advanced file context trees, restore points... i can keep going on semantic code search and context trees are the future of agentic coding and this tool proves it the feature i loved the most is semantic search and how it gets things done 2x faster with least possible tokens it makes an agent that actually knows what it’s doing and not just guessing, it makes meaning from your code similar to RAG. if you aren't optimizing your context, you are just burning money the developer says this tool is still under development, it can have unexpected behavior and the docs need updates but the video shows the reality of how fast it can be github: get here:

forloop

226,544 просмотров • 7 месяцев назад