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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... show more

Kalsoom (ghotai )

51,049 subscribers

118,728 次观看 • 10 天前 •via X (Twitter)

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18 条评论

Kalsoom (ghotai ) 的头像

Kalsoom (ghotai )10 天前

Try Qoder free with 600 Credits 👇 300 from the Pro trial + 300 with my code 9797YB.

Patrick's AIBuzzNews 的头像

Patrick's AIBuzzNews10 天前

That's the smarter and more efficent way.

Shah 的头像

Shah10 天前

This a really smart setup

Fakhr 的头像

Fakhr10 天前

600 credits plus Qwen 3.8 Flash at 0.0x Credits makes this worth testing.

NOVA 的头像

NOVA10 天前

This is the kind of repetitive research task AI should take off your plate

AIMATRIX 的头像

AIMATRIX10 天前

The research → analysis → report workflow is the part that really caught my attention. Huge time saver.

Hussain Hashim | Building SundayBack 的头像

Hussain Hashim | Building SundayBack10 天前

@AIwithGhotai nice! Make sure to set an alert for industry events too. Sometimes that's where the big news drops.

Jimmy 的头像

Jimmy10 天前

Research → analysis → report, all handled by one workflow. That’s the goal

Abdul Sarfraj 的头像

Abdul Sarfraj10 天前

This is a great example of using AI for actual workflow automation, not just content generation

Ujala Pandey 的头像

Ujala Pandey10 天前

The structured report at the end is what makes this workflow really practical

David Marco 的头像

David Marco10 天前

The combination of X, Reddit, Product Hunt, and official sources makes this much more useful than basic web search

Vikas gupta 的头像

Vikas gupta10 天前

The weekly competitor research alone can eat up so much time. Automating the whole loop makes a lot of sense

Sachin Malik 的头像

Sachin Malik10 天前

Competitor monitoring is perfect for this kind of recurring AI workflow

Rohit 的头像

Rohit10 天前

Recurring research tasks are one of the most underrated AI automation use cases

Rachel Woods 的头像

Rachel Woods10 天前

I like that this goes beyond just collecting links. It researches, analyzes, and turns everything into a report

EDDY VU 的头像

EDDY VU10 天前

Tracking Reddit and X usually pulls in tons of noise, how clean is the signal you get back?

Lily 的头像

Lily10 天前

The “set it once” part is exactly what makes workflows like this valuable

JOYA 的头像

JOYA10 天前

Tracking official updates + X + Reddit + Product Hunt in one workflow is seriously useful

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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.

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 次观看 • 12 天前

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.

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 次观看 • 10 天前

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 permissions, then let it run. What I could actually watch: = Qoder planning the task = Reading the project context = Executing commands and making file changes = Working through the steps instead of stopping at an answer = Verifying the result at the end The interesting part is that I can still watch the process, adjust the direction, pause it, or take over when needed. I also tested Qwen3.8-Flash, which is currently showing 0.0x Credits - meaning it’s free for all users through September 30. New users can get: - 300 Credits from the 14-day Pro trial - 300 additional Credits with my creator code Enter C46T6F here first: Then download Qoder: My first impression: the real value isn’t just generating code, it’s giving the agent an outcome and letting it actually work toward a verified result.

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 permissions, then let it run. What I could actually watch: = Qoder planning the task = Reading the project context = Executing commands and making file changes = Working through the steps instead of stopping at an answer = Verifying the result at the end The interesting part is that I can still watch the process, adjust the direction, pause it, or take over when needed. I also tested Qwen3.8-Flash, which is currently showing 0.0x Credits - meaning it’s free for all users through September 30. New users can get: - 300 Credits from the 14-day Pro trial - 300 additional Credits with my creator code Enter C46T6F here first: Then download Qoder: My first impression: the real value isn’t just generating code, it’s giving the agent an outcome and letting it actually work toward a verified result.

Ethan Cole AI

251,390 次观看 • 9 天前

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.

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 次观看 • 13 天前

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

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 次观看 • 11 天前

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.

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

143,075 次观看 • 8 天前

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.

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 次观看 • 8 天前

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.

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 次观看 • 13 天前

I launched Grok Bot for 24 hours, gave it 3 jobs, and stopped treating AI like a chatbot. I had it scan live X + web data for emerging AI products, research what people were actually saying about them, filter the obvious engagement bait, compare sources and turn the strongest signals into a structured brief. At the same time, I could have separate research paths looking at competitors, code or market opportunities instead of forcing everything through one conversation. That’s the difference I underestimated. I don’t want to sit inside a chat writing another prompt every 5 minutes. I want to define the objective, give the agent access to the right tools and data, let it execute, then step back in when judgment or approval is actually needed. The old workflow was prompt → answer → copy → repeat. The workflow I’m moving toward is assign → execute → verify. Once AI stops waiting for your next message, it stops feeling like software you use and starts feeling like work you delegated.

I launched Grok Bot for 24 hours, gave it 3 jobs, and stopped treating AI like a chatbot. I had it scan live X + web data for emerging AI products, research what people were actually saying about them, filter the obvious engagement bait, compare sources and turn the strongest signals into a structured brief. At the same time, I could have separate research paths looking at competitors, code or market opportunities instead of forcing everything through one conversation. That’s the difference I underestimated. I don’t want to sit inside a chat writing another prompt every 5 minutes. I want to define the objective, give the agent access to the right tools and data, let it execute, then step back in when judgment or approval is actually needed. The old workflow was prompt → answer → copy → repeat. The workflow I’m moving toward is assign → execute → verify. Once AI stops waiting for your next message, it stops feeling like software you use and starts feeling like work you delegated.

Lummox

3,955,848 次观看 • 1 个月前

I just tried ScienceBuddy, a free AI research workspace from PhAI Labs. I uploaded research documents and asked it to analyze the evidence and build a structured table. What stood out: → Plans the research task → Retrieves relevant sources → Runs research tools and analysis → Shows the steps in the Trajectory panel → Gives you a structured final result You can actually inspect what happened instead of just getting a final answer. Try it: Research paper: Technical report: GitHub: ScienceBuddy is currently a Preview release. Please verify results yourself before using them in research. #ScienceBuddy #PhAILabs #AIforScience

I just tried ScienceBuddy, a free AI research workspace from PhAI Labs. I uploaded research documents and asked it to analyze the evidence and build a structured table. What stood out: → Plans the research task → Retrieves relevant sources → Runs research tools and analysis → Shows the steps in the Trajectory panel → Gives you a structured final result You can actually inspect what happened instead of just getting a final answer. Try it: Research paper: Technical report: GitHub: ScienceBuddy is currently a Preview release. Please verify results yourself before using them in research. #ScienceBuddy #PhAILabs #AIforScience

Aryan Rakib

62,676 次观看 • 12 天前

Why did spec-driven development never take off - the workflow tools like Amazon’s Kiro or GitHub Workspaces encouraged? dex, founder of HumanLayer: “These projects have a really interesting idea: you maintain a set of specifications for your software, and then you maintain the code itself, and the dream is the coding part is just compiling specs into code. But that part never really materialized. I’m on a GitHub issue in spec kit that’s been open for a year, and every couple of weeks I get a new email on the thread of people complaining about the same problem: I edit my specs and then I edit the code, and the code drifts from the specs. How do I keep the specs up to date as the code is changing? You now have two sources of truth, and it stops being useful. That’s why with RPI (Research, Plan, Implement), the docs are tactical execution docs - I do the research, the plan, the implementation, and I throw the docs out. The next time I need research I just do it from scratch, because tokens are cheap and my time is expensive.”

Why did spec-driven development never take off - the workflow tools like Amazon’s Kiro or GitHub Workspaces encouraged? dex, founder of HumanLayer: “These projects have a really interesting idea: you maintain a set of specifications for your software, and then you maintain the code itself, and the dream is the coding part is just compiling specs into code. But that part never really materialized. I’m on a GitHub issue in spec kit that’s been open for a year, and every couple of weeks I get a new email on the thread of people complaining about the same problem: I edit my specs and then I edit the code, and the code drifts from the specs. How do I keep the specs up to date as the code is changing? You now have two sources of truth, and it stops being useful. That’s why with RPI (Research, Plan, Implement), the docs are tactical execution docs - I do the research, the plan, the implementation, and I throw the docs out. The next time I need research I just do it from scratch, because tokens are cheap and my time is expensive.”

The Pragmatic Engineer

170,120 次观看 • 2 个月前

I wanted a better understanding of the AI agent market. So I gave Viktor a task: "Research the Al agent market and generate a competitor report covering the leading platforms, pricing, core capabilities, target customers, differentiators, and recent product launches. Include a comparison table and key industry trends." Instead of spending hours collecting information across different sources, Viktor helped organize the research process and generate a structured competitor report. The output included: - Side-by-side comparison tables - Market summaries - Competitor breakdowns - Industry trend analysis What stood out wasn't just the research. It was being able to run the workflow directly inside Slack without constantly switching between tools. Viktor connects to 3,200+ tools and helps execute reports, dashboards, code, campaigns, and other multi-step workflows. Viktor feels like a step in that direction. Try it here:

I wanted a better understanding of the AI agent market. So I gave Viktor a task: "Research the Al agent market and generate a competitor report covering the leading platforms, pricing, core capabilities, target customers, differentiators, and recent product launches. Include a comparison table and key industry trends." Instead of spending hours collecting information across different sources, Viktor helped organize the research process and generate a structured competitor report. The output included: - Side-by-side comparison tables - Market summaries - Competitor breakdowns - Industry trend analysis What stood out wasn't just the research. It was being able to run the workflow directly inside Slack without constantly switching between tools. Viktor connects to 3,200+ tools and helps execute reports, dashboards, code, campaigns, and other multi-step workflows. Viktor feels like a step in that direction. Try it here:

Utkarsh Sharma

40,338 次观看 • 3 个月前

And here's today's AI news from X as a video. In an hour I had my AI agents read all of tech on X, find all the important news, papers, and much more. Write a report. Bring that report over to @NotebookLM. Then create all this and post it back to you. It's all X.

And here's today's AI news from X as a video. In an hour I had my AI agents read all of tech on X, find all the important news, papers, and much more. Write a report. Bring that report over to @NotebookLM. Then create all this and post it back to you. It's all X.

Robert Scoble

36,408 次观看 • 6 个月前

Nobody will see this but this is how I do research 10x faster now > I drop a topic into Claude Code > It finds 10 relevant YouTube sources automatically > Sends them to NotebookLM 6 minutes later I get a full structured analysis, an infographic, and a markdown file saved to my vault Before this I was spending 3 hours doing the same thing manually and ending up with messy notes I'd never look at again Now the system does it in one command and the output gets better every time I use it full setup guide in the article

Nobody will see this but this is how I do research 10x faster now > I drop a topic into Claude Code > It finds 10 relevant YouTube sources automatically > Sends them to NotebookLM 6 minutes later I get a full structured analysis, an infographic, and a markdown file saved to my vault Before this I was spending 3 hours doing the same thing manually and ending up with messy notes I'd never look at again Now the system does it in one command and the output gets better every time I use it full setup guide in the article

monokern

188,949 次观看 • 4 个月前

15 AI tools researched, ranked, and turned into X-ready content in ~2 minutes. I gave Viktor one job: Find the best new AI tools for our audience, research them, rank the top 3, and turn the research into X-ready content. In about 2 minutes, Viktor ran 24 targeted searches, researched 15 tools, cross-checked pricing, scored the options, built the comparison, and produced the final post. My first attempt didn't work. The workflow stalled before Viktor could connect the research and content steps. Then I adjusted the task, and that's when things clicked. What surprised me wasn't that he could research. It was how much of the workflow he could actually handle. Research. Compare. Prioritize. Create. Instead of jumping between tabs, collecting information, comparing tools, and turning research into content manually, Viktor handled the workflow in one place. That's the part I think people miss about AI employees. The value isn't another chatbot that gives you an answer. It's having an AI teammate that can take on real work and fit into the way your team already operates. Viktor works inside Slack and Microsoft Teams and connects to 3,200+ tools. A copilot helps you work. An AI employee works when you don't. I think this is where AI employees get genuinely interesting: the question isn't whether they can answer you, but whether you can trust them with actual work. Hire Viktor for your team. $100 in credits included, no card. Full link in first comment. #AIemployee #AITeams Paid Partnership

15 AI tools researched, ranked, and turned into X-ready content in ~2 minutes. I gave Viktor one job: Find the best new AI tools for our audience, research them, rank the top 3, and turn the research into X-ready content. In about 2 minutes, Viktor ran 24 targeted searches, researched 15 tools, cross-checked pricing, scored the options, built the comparison, and produced the final post. My first attempt didn't work. The workflow stalled before Viktor could connect the research and content steps. Then I adjusted the task, and that's when things clicked. What surprised me wasn't that he could research. It was how much of the workflow he could actually handle. Research. Compare. Prioritize. Create. Instead of jumping between tabs, collecting information, comparing tools, and turning research into content manually, Viktor handled the workflow in one place. That's the part I think people miss about AI employees. The value isn't another chatbot that gives you an answer. It's having an AI teammate that can take on real work and fit into the way your team already operates. Viktor works inside Slack and Microsoft Teams and connects to 3,200+ tools. A copilot helps you work. An AI employee works when you don't. I think this is where AI employees get genuinely interesting: the question isn't whether they can answer you, but whether you can trust them with actual work. Hire Viktor for your team. $100 in credits included, no card. Full link in first comment. #AIemployee #AITeams Paid Partnership

Marry Evan

30,072 次观看 • 1 个月前

I'm excited to introduce my FREE AI Pandas Data Analyst Copilot which created a data analysis report with dozens of charts from my questions in under 30 seconds. Today, I'll share with you how to automate data analysis with my Pandas AI Agent + Copilot, which is available on GitHub. I'll guide you through setting up the Copilot app, creating dozens of data analysis charts from any CSV or Excel file, and interacting with your data live. This AI is a BIG help! Table of Contents: 00:00 Introduction to the App 02:24 Setting Up the App 04:25 Using the App 08:58 Understanding the Python Code Github to AI Data Science Team (app is in the apps folder): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: === Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, April 9th, I'm sharing one of my best AI Projects: Time Series Forecasting with AI Register here (500 Seats):

I'm excited to introduce my FREE AI Pandas Data Analyst Copilot which created a data analysis report with dozens of charts from my questions in under 30 seconds. Today, I'll share with you how to automate data analysis with my Pandas AI Agent + Copilot, which is available on GitHub. I'll guide you through setting up the Copilot app, creating dozens of data analysis charts from any CSV or Excel file, and interacting with your data live. This AI is a BIG help! Table of Contents: 00:00 Introduction to the App 02:24 Setting Up the App 04:25 Using the App 08:58 Understanding the Python Code Github to AI Data Science Team (app is in the apps folder): Get the Code and Future Updates by Joining my Python AI/ML Tips Newsletter: === Want to learn how to build AI projects companies actually want? (live Python Code) On Wednesday, April 9th, I'm sharing one of my best AI Projects: Time Series Forecasting with AI Register here (500 Seats):

Matt Dancho (Business Science)

12,736 次观看 • 1 年前

$I just realized I've been scheduling my biggest AI jobs at the worst possible time, and it was costing me up to 80% more than it needed to. Qoder now runs Qwen3.7-Max and Qwen3.7-Plus with off-peak rates, which means the same models cost significantly less during the off-peak window. here's what changed: • Qwen3.7-Max: 0.5x → 0.1x (up to 80% off) • Qwen3.7-Plus: 0.1x → 0.04x (60% off) the model doesn't change. the reasoning doesn't change. the context length doesn't change. only the credit multiplier does. and the best part? the off-peak rates are applied automatically. no configuration needed. so instead of launching long refactors, migrations, agent runs, or large code generation whenever I think about them... I simply queue them during my off-peak window ( to local time) and let the same flagship Qwen model do the work for a fraction of the usual cost. that's probably the easiest productivity upgrade I've made this month. switch to Qwen3.7-Max or Qwen3.7-Plus in Qoder during your local off-peak hours and compare the credits before and after. search "Google Qoder" or download it here:$

I just realized I've been scheduling my biggest AI jobs at the worst possible time, and it was costing me up to 80% more than it needed to. Qoder now runs Qwen3.7-Max and Qwen3.7-Plus with off-peak rates, which means the same models cost significantly less during the off-peak window. here's what changed: • Qwen3.7-Max: 0.5x → 0.1x (up to 80% off) • Qwen3.7-Plus: 0.1x → 0.04x (60% off) the model doesn't change. the reasoning doesn't change. the context length doesn't change. only the credit multiplier does. and the best part? the off-peak rates are applied automatically. no configuration needed. so instead of launching long refactors, migrations, agent runs, or large code generation whenever I think about them... I simply queue them during my off-peak window ( to local time) and let the same flagship Qwen model do the work for a fraction of the usual cost. that's probably the easiest productivity upgrade I've made this month. switch to Qwen3.7-Max or Qwen3.7-Plus in Qoder during your local off-peak hours and compare the credits before and after. search "Google Qoder" or download it here:

marcus

24,620 次观看 • 3 个月前

Is Windsurf better than Cursor? impossible to say, but... it does one thing better: soooo... I spent an hour testing windsurf... and this is what i created... and app called "boop-boop" lol... one thing windsurf does is it searches the internet. If you've been watching my videos you know that i go to perplexity all the time to get code examples and docs. but with windsurf you can just say, "search the replicate top image gen models and add them to the chat we just created" It will search the internet, find the right model then write the code. This is great for replicate and openrouter. You can have one api key in your .env file then just tell windsurf to search for docs for [insert ai model] and it will just add it to your app. Impressed tbh.

Is Windsurf better than Cursor? impossible to say, but... it does one thing better: soooo... I spent an hour testing windsurf... and this is what i created... and app called "boop-boop" lol... one thing windsurf does is it searches the internet. If you've been watching my videos you know that i go to perplexity all the time to get code examples and docs. but with windsurf you can just say, "search the replicate top image gen models and add them to the chat we just created" It will search the internet, find the right model then write the code. This is great for replicate and openrouter. You can have one api key in your .env file then just tell windsurf to search for docs for [insert ai model] and it will just add it to your app. Impressed tbh.

Riley Brown

31,043 次观看 • 1 年前

This is how Opus 5.5 in Claude Code built a WoW-style game in 36h. There's a lot more to it than one prompt. I wrote a detailed spec, gave it access to Unity, Blender and Higgsfield, and packed my own workflow into skills. I go through all of it in the video:

This is how Opus 5.5 in Claude Code built a WoW-style game in 36h. There's a lot more to it than one prompt. I wrote a detailed spec, gave it access to Unity, Blender and Higgsfield, and packed my own workflow into skills. I go through all of it in the video:

Stefan 3D AI

28,628 次观看 • 7 天前

Better Cursor alternative? I've been playing with the new AI IDE Windsurf launched by Codeium past few days, and the result was impressive, particularly: 1. Code base understanding: One thing Windsurf did really well is its understanding of the whole code base & dependencies; This led to a big difference in terms of final code quality 2. Context aware: It actually knows everything I did in the editor, e.g. it automatically knows code edits I made without me prompt it; 3. Reflection: I noticed Windsurf has been finetuned to reflect and try to iterate/improve code it generated autonomously; This was a magic moment for me .... I shared key highlights i had and showcase my process of building a PDF converter product using windsurf 0:00 Platform overview 6:21 How does context aware engine works 10:45 Case study: Build PDF converter Sass ---- If you have any further question or want to get deep dive into more AI coding, you can join my community where I post tips weekly:

Better Cursor alternative? I've been playing with the new AI IDE Windsurf launched by Codeium past few days, and the result was impressive, particularly: 1. Code base understanding: One thing Windsurf did really well is its understanding of the whole code base & dependencies; This led to a big difference in terms of final code quality 2. Context aware: It actually knows everything I did in the editor, e.g. it automatically knows code edits I made without me prompt it; 3. Reflection: I noticed Windsurf has been finetuned to reflect and try to iterate/improve code it generated autonomously; This was a magic moment for me .... I shared key highlights i had and showcase my process of building a PDF converter product using windsurf 0:00 Platform overview 6:21 How does context aware engine works 10:45 Case study: Build PDF converter Sass ---- If you have any further question or want to get deep dive into more AI coding, you can join my community where I post tips weekly:

Jason Zhou

64,713 次观看 • 1 年前