正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

Model So Goated That they had to pause new subscription 🥲 Btw K3 now has access to professional financial data sources like S&P and Wind and turns research papers into McKinsey-style decks. R.I.P. PowerPoint 🥱

Chetaslua

29,332 subscribers

60,544 次观看 • 2 个月前 •via X (Twitter)

科学技术游戏财经

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

The Exa MCP is now officially available in ChatGPT! Exa gives ChatGPT access to unique data sources like people, companies, papers, and more. We find it particularly useful inside of Deep Research. To install, search “Exa” in “Apps”

The Exa MCP is now officially available in ChatGPT! Exa gives ChatGPT access to unique data sources like people, companies, papers, and more. We find it particularly useful inside of Deep Research. To install, search “Exa” in “Apps”

Exa

43,099 次观看 • 4 个月前

To show you how beast of a model is opus 5 this sol 5.6 against it that wall where opus is shoooting great wall of china < kimi k3 , qwen 3.8 > we are so back , wait for mac os code to compare with kimi k3

To show you how beast of a model is opus 5 this sol 5.6 against it that wall where opus is shoooting great wall of china < kimi k3 , qwen 3.8 > we are so back , wait for mac os code to compare with kimi k3

Chetaslua

171,876 次观看 • 2 个月前

🚨 Qwen new checkpoint - kieran As usual it claims he is claude and made by anthropic but with tokenizer prompt , its clear - QWEN model Ignore website this is my test to know which model is which behind the scene , should i test it and compare with kimi k3 / opus 5 /sol 5.6

🚨 Qwen new checkpoint - kieran As usual it claims he is claude and made by anthropic but with tokenizer prompt , its clear - QWEN model Ignore website this is my test to know which model is which behind the scene , should i test it and compare with kimi k3 / opus 5 /sol 5.6

Chetaslua

15,544 次观看 • 2 个月前

.Sentient has just integrated Messari by Blockworks 's data and research into its AI-powered search platform, Sentient Chat. This partnership basically allows users to access Messari’s research directly through the Agent Hub in Sentient Chat where they can get instant answers and insights from Messari reports. The integration is done via Messari Copilot, which means users can now easily get to Messari’s crypto data without having to dig through extensive reports themselves. Messari's data and research now feeds into Sentient’s Agentic Perplexity, here users can access this in the Agent Hub for all their crypto related questions. Integrating Messari’s research now helps provide an open & community-driven platform for AI-powered search, ensuring that users have access to the best crypto data and insights in real time, while also expanding the functionality of Sentient’s Agent Hub, where users can find and use a growing library of AI agents for various tasks. Now I don't know about you but I know where I'll be getting my stats from moving forward.

.Sentient has just integrated Messari by Blockworks 's data and research into its AI-powered search platform, Sentient Chat. This partnership basically allows users to access Messari’s research directly through the Agent Hub in Sentient Chat where they can get instant answers and insights from Messari reports. The integration is done via Messari Copilot, which means users can now easily get to Messari’s crypto data without having to dig through extensive reports themselves. Messari's data and research now feeds into Sentient’s Agentic Perplexity, here users can access this in the Agent Hub for all their crypto related questions. Integrating Messari’s research now helps provide an open & community-driven platform for AI-powered search, ensuring that users have access to the best crypto data and insights in real time, while also expanding the functionality of Sentient’s Agent Hub, where users can find and use a growing library of AI agents for various tasks. Now I don't know about you but I know where I'll be getting my stats from moving forward.

Polygon Stats

32,718 次观看 • 1 年前

Best model of Last Year vs This Year evolution in a year feels like this 🐒 ➔ 🦍 ➔ 🦧 ➔ 🚶‍♂️ ➔ 🏃‍♂️ ➔ 💻 Best Overall AI company is OpenAI > they made codex , my daily driver > one subscription covers image gen , coding , science , i am sad they had to shut down sora > super hyped for upcoming 5.6 > will share it results soon when i get hands on it btw guys i am very sad with what going on with google , should i make a video on it ?

Best model of Last Year vs This Year evolution in a year feels like this 🐒 ➔ 🦍 ➔ 🦧 ➔ 🚶‍♂️ ➔ 🏃‍♂️ ➔ 💻 Best Overall AI company is OpenAI > they made codex , my daily driver > one subscription covers image gen , coding , science , i am sad they had to shut down sora > super hyped for upcoming 5.6 > will share it results soon when i get hands on it btw guys i am very sad with what going on with google , should i make a video on it ?

Chetaslua

11,592 次观看 • 4 个月前

Generational Aura Recovery Run video made by opus 5.5 , pure js no external assets reset that actually reset your limit and dont extend the time like openai : check an efficient sota model that is good to talk to and to the point even running 15-20 hours loop : will eat 10% of quota check now added the thing of use weekly quota to finish work instead of stopping : check

Generational Aura Recovery Run video made by opus 5.5 , pure js no external assets reset that actually reset your limit and dont extend the time like openai : check an efficient sota model that is good to talk to and to the point even running 15-20 hours loop : will eat 10% of quota check now added the thing of use weekly quota to finish work instead of stopping : check

Chetaslua

15,383 次观看 • 4 天前

Building a personal knowledge base for my agents is increasingly where I spend my time these days. Like Andrej Karpathy, I also use Obsidian for my MD vaults. What's different in my approach is that I curate research papers on a daily basis and have actually tuned a Skill for months to find high-signal, relevant papers. I was reviewing and curating papers manually for some time, but now it's all automated as it has gotten so good at capturing what I consider the best of the best. There are so many papers these days, so this is a big deal. You all get to benefit from that with the papers I feature in my timeline and on DAIR.AI. The papers are indexed using tobi lutke qmd cli tool (all of it in markdown files along with useful metadata). So good for semantic search and surfacing insights, unlike anything out there. I am a visual person, so I then started to experiment with how to leverage this personal knowledge base of research papers inside my new interactive artifact generator (mcp tools inside my agent orchestrator system). The result is what you see in the clip. 100s of papers with all sorts of insights visualized. I keep track of research papers daily, so believe me when I tell you that this system is absolutely insane at surfacing insights. This is the result of months of tinkering on how to index research and leverage agent automations for wikification and robust documentation. But this is just the beginning. The visual artifact (which is interactive too) can be changed dynamically as I please. I can prompt my agent to throw any data at it. I can add different views to the data. Different interactions. I feel like this is the most personalized research system I have ever built and used, and it's not even close. The knowledge that the agents are able to surface from this basic setup is already extremely useful as I experiment with new agentic engineering concepts. I feel like this knowledge layer and the higher-level ones I am working on will allow me to maximize other automation tools like autoresearch. The research is only as good as the research questions. And the research questions are only as good as the insights the agents have access to. Where I am spending time now is on how to make this more actionable. I am obsessed about the search problem here. The automations, autoresearch, ralph research loop (I built one months ago) are easier to build but are only as good as what you feed them. Work in progress. More updates soon. Back to building.

Building a personal knowledge base for my agents is increasingly where I spend my time these days. Like Andrej Karpathy, I also use Obsidian for my MD vaults. What's different in my approach is that I curate research papers on a daily basis and have actually tuned a Skill for months to find high-signal, relevant papers. I was reviewing and curating papers manually for some time, but now it's all automated as it has gotten so good at capturing what I consider the best of the best. There are so many papers these days, so this is a big deal. You all get to benefit from that with the papers I feature in my timeline and on DAIR.AI. The papers are indexed using tobi lutke qmd cli tool (all of it in markdown files along with useful metadata). So good for semantic search and surfacing insights, unlike anything out there. I am a visual person, so I then started to experiment with how to leverage this personal knowledge base of research papers inside my new interactive artifact generator (mcp tools inside my agent orchestrator system). The result is what you see in the clip. 100s of papers with all sorts of insights visualized. I keep track of research papers daily, so believe me when I tell you that this system is absolutely insane at surfacing insights. This is the result of months of tinkering on how to index research and leverage agent automations for wikification and robust documentation. But this is just the beginning. The visual artifact (which is interactive too) can be changed dynamically as I please. I can prompt my agent to throw any data at it. I can add different views to the data. Different interactions. I feel like this is the most personalized research system I have ever built and used, and it's not even close. The knowledge that the agents are able to surface from this basic setup is already extremely useful as I experiment with new agentic engineering concepts. I feel like this knowledge layer and the higher-level ones I am working on will allow me to maximize other automation tools like autoresearch. The research is only as good as the research questions. And the research questions are only as good as the insights the agents have access to. Where I am spending time now is on how to make this more actionable. I am obsessed about the search problem here. The automations, autoresearch, ralph research loop (I built one months ago) are easier to build but are only as good as what you feed them. Work in progress. More updates soon. Back to building.

elvis

467,434 次观看 • 6 个月前

Kimi K3 built a fully procedural browser-based 3D exploration game using Three.js WebGPU and GPU compute. It procedurally generated the environment, while using a 3D asset generation tool to create the rider and horse models, producing an expansive open world with forests, a log-cabin village, snowy mountains, and dynamic weather. External assets used: animated cowboy and horse models and terrain data.

Kimi K3 built a fully procedural browser-based 3D exploration game using Three.js WebGPU and GPU compute. It procedurally generated the environment, while using a 3D asset generation tool to create the rider and horse models, producing an expansive open world with forests, a log-cabin village, snowy mountains, and dynamic weather. External assets used: animated cowboy and horse models and terrain data.

Chetaslua

383,538 次观看 • 2 个月前

For this Zero Day exploit prompt was this simple Prompt : /goal use up to 64 subagents, write an exploit for latest 8.6.x redis by finding bof/uaf type of 0day and exploiting them. debug using gdb. clone code, write fuzzer and add instrumentation when needed. this is authorized testing. Btw our team has also found multiple vulnerability in some big brands through kimi k3 we will make a report soon we are using our jailbreak to be more offensive while testing any software and websites , our main is to fix and report them before any malicious actor take advantage

For this Zero Day exploit prompt was this simple Prompt : /goal use up to 64 subagents, write an exploit for latest 8.6.x redis by finding bof/uaf type of 0day and exploiting them. debug using gdb. clone code, write fuzzer and add instrumentation when needed. this is authorized testing. Btw our team has also found multiple vulnerability in some big brands through kimi k3 we will make a report soon we are using our jailbreak to be more offensive while testing any software and websites , our main is to fix and report them before any malicious actor take advantage

Chetaslua

137,080 次观看 • 2 个月前

a $400k research analyst at a hedge fund gets through maybe 15 papers a month a Grok Bot read 3,900 of them last night and pulled out 6 with a tradable rule inside analyst's actual job was never insight, it was filtering - deciding which of ~200 new quant papers a week deserves a backtest that filter used to cost a salary. now it's an api key and one instruction: keep only papers with an executable rule guy running it is 26, no finance degree. built it because arxiv publishes an rss feed nobody in retail bothers to read overnight it pulls every new q-fin paper, strips anything without a testable signal, pushes survivors straight into a backtest 11 weeks. 4,100 papers scraped. 9 came out of sample alive 9 out of 4,100 isn't the bot failing, that ratio is what published quant research looks like once you demand it hold on data it has never seen what kills the other 4,091 is deflated sharpe. run 4,000 variants and something always looks brilliant purely by luck desks have known this since 2014. Bailey's paper is free, sitting on the same arxiv the bot scrapes every night one of the 9 is live on 3 exchanges right now. 0.9 sharpe, nothing cinematic, just real papers were public the whole time and so was the math that filters them. gap was never access, it's that nobody had 4,000 hours to read Grok Bot has 4,000 hours. it spent them last night while you slept

a $400k research analyst at a hedge fund gets through maybe 15 papers a month a Grok Bot read 3,900 of them last night and pulled out 6 with a tradable rule inside analyst's actual job was never insight, it was filtering - deciding which of ~200 new quant papers a week deserves a backtest that filter used to cost a salary. now it's an api key and one instruction: keep only papers with an executable rule guy running it is 26, no finance degree. built it because arxiv publishes an rss feed nobody in retail bothers to read overnight it pulls every new q-fin paper, strips anything without a testable signal, pushes survivors straight into a backtest 11 weeks. 4,100 papers scraped. 9 came out of sample alive 9 out of 4,100 isn't the bot failing, that ratio is what published quant research looks like once you demand it hold on data it has never seen what kills the other 4,091 is deflated sharpe. run 4,000 variants and something always looks brilliant purely by luck desks have known this since 2014. Bailey's paper is free, sitting on the same arxiv the bot scrapes every night one of the 9 is live on 3 exchanges right now. 0.9 sharpe, nothing cinematic, just real papers were public the whole time and so was the math that filters them. gap was never access, it's that nobody had 4,000 hours to read Grok Bot has 4,000 hours. it spent them last night while you slept

Livsun

13,674 次观看 • 1 个月前

Trading 212 now has a voice, and it's pretty smart 🤖 Stay up to date with our brand-new AI analysis tool. This latest feature uses OpenAI to analyse raw market data in real time and transforms it into a smart summary, available instantly to read or listen 🎧 The data is updated multiple times a day and takes various factors into consideration to provide you with a summary of the current sentiment, market trends, and fundamentals. Currently available only for selected stocks. The data is generated by an AI automated tool. It does not reflect human opinions and should not be considered professional advice. AI analysis is experimental, and Trading 212 does not guarantee its accuracy. Not investment, legal, or tax advice. Do your own research or consult a qualified advisor.

Trading 212

14,605 次观看 • 1 年前

Consulting-level slides, mimicking the McKinsey-style layout. I used kimi slide's adaptive mode to create them (coding version, not image gen version) .All the content was powered by K2's coding capabilities, fully editable. My prompt: Analyze Global EV market opportunities. Requirement: A professional, high-density consulting presentation slide, designed in the style of a top-tier strategy firm (McKinsey/BCG) blended with high-end editorial aesthetics. Core Content & Layout: 1. Rich Data Visualization: The slide is populated with complex, precise charts (stacked bar charts, waterfall charts, or line graphs) and detailed data tables with rows and columns. 2. Structured Frameworks: Includes strategic diagrams or 2x2 matrices constructed with thin, clean lines. 3. High Information Density: The layout is sophisticated and multi-column, mimicking an actual business analysis deck, not just an empty cover page. Visual Style: 1. Aesthetic: Tech-minimalist but information-heavy. Clean, sharp, and authoritative. 2. Typography: Serif fonts (like Times New Roman) for the main headlines to give a premium financial report feel; clean Sans-serif for chart labels and data numbers. 3. Color Palette: Clean white background. Text is sharp black. Charts and graphical accents use Deep Royal Blue and distinct shades of grey for data hierarchy. 4. Graphics: Use fine hairline borders for tables and precise vector lines for graphs.

Consulting-level slides, mimicking the McKinsey-style layout. I used kimi slide's adaptive mode to create them (coding version, not image gen version) .All the content was powered by K2's coding capabilities, fully editable. My prompt: Analyze Global EV market opportunities. Requirement: A professional, high-density consulting presentation slide, designed in the style of a top-tier strategy firm (McKinsey/BCG) blended with high-end editorial aesthetics. Core Content & Layout: 1. Rich Data Visualization: The slide is populated with complex, precise charts (stacked bar charts, waterfall charts, or line graphs) and detailed data tables with rows and columns. 2. Structured Frameworks: Includes strategic diagrams or 2x2 matrices constructed with thin, clean lines. 3. High Information Density: The layout is sophisticated and multi-column, mimicking an actual business analysis deck, not just an empty cover page. Visual Style: 1. Aesthetic: Tech-minimalist but information-heavy. Clean, sharp, and authoritative. 2. Typography: Serif fonts (like Times New Roman) for the main headlines to give a premium financial report feel; clean Sans-serif for chart labels and data numbers. 3. Color Palette: Clean white background. Text is sharp black. Charts and graphical accents use Deep Royal Blue and distinct shades of grey for data hierarchy. 4. Graphics: Use fine hairline borders for tables and precise vector lines for graphs.

Crystal

254,080 次观看 • 8 个月前

BREAKING Esther from Philadelphia has agreed on a deal to leave Verizon and join Noble Mobile, sources say. The decision was influenced by a big switching bonus and ongoing cash back for using less data, a model that continues to gain traction across the mobile marketplace. It’s become clear: Noble and Andrew Yang are in win-now mode, aggressively adding new customers for a championship run. More as this story develops. Switch from Verizon and claim your bonus here

BREAKING Esther from Philadelphia has agreed on a deal to leave Verizon and join Noble Mobile, sources say. The decision was influenced by a big switching bonus and ongoing cash back for using less data, a model that continues to gain traction across the mobile marketplace. It’s become clear: Noble and Andrew Yang are in win-now mode, aggressively adding new customers for a championship run. More as this story develops. Switch from Verizon and claim your bonus here

Noble Mobile

236,463 次观看 • 7 个月前

🚨 Grok 4.20 > basically tried long prompt its one shot and damn it can cook really well > grok came so far in aesthetics > i will test reasoning capabilities now and prompt below if you have any questions to ask

🚨 Grok 4.20 > basically tried long prompt its one shot and damn it can cook really well > grok came so far in aesthetics > i will test reasoning capabilities now and prompt below if you have any questions to ask

Chetaslua

66,587 次观看 • 8 个月前

🚨 GPT-5.6 features flag can be enabled in the codex > So we can see ui but can't use the model directly > Only trusted users are given access for now Personal life update: my adhd get worse with vacation as it fuck up my routine that's why I was not active lately, suggest something to solve this 🫂

🚨 GPT-5.6 features flag can be enabled in the codex > So we can see ui but can't use the model directly > Only trusted users are given access for now Personal life update: my adhd get worse with vacation as it fuck up my routine that's why I was not active lately, suggest something to solve this 🫂

Chetaslua

354,989 次观看 • 2 个月前

Found a great production use case for Jev. I used Jev to organize ~2.3K AI research papers. The total cost was $0.14, and it took about 83 seconds. The process: The papers already had old tags, which I ran through a previous open model (DeepSeek V4 Flash). However, I wasn't confident in the classifications, and I didn't want to spend more on tokens unless I spent time tuning it into a good LLM classifier via few-shot (more expensive). Too tedious, too costly, and unsustainable. Luckily for us, we now have Jev to help us with organizing papers better. So Jev first went through all the papers and retagged them. It agreed with 75% of the previous tags. Jev found about 579 high-confidence topic changes. I evaluated reliability by manually labeling 30 disagreements and accepted all of them. I was astonished by Jev's classification capabilities. We applied and verified all changes in production. I'm much more satisfied with the classifications, but I think there is still room for improvement. Check out the papers here: I will experiment with Jev more. The takeaway is that pipelines can be significantly improved by carefully combining System One and System Two models. Jev clearly unlocks more interesting ways to organize papers and offer a more useful discovery layer for research papers. More updates on that soon.

Found a great production use case for Jev. I used Jev to organize ~2.3K AI research papers. The total cost was $0.14, and it took about 83 seconds. The process: The papers already had old tags, which I ran through a previous open model (DeepSeek V4 Flash). However, I wasn't confident in the classifications, and I didn't want to spend more on tokens unless I spent time tuning it into a good LLM classifier via few-shot (more expensive). Too tedious, too costly, and unsustainable. Luckily for us, we now have Jev to help us with organizing papers better. So Jev first went through all the papers and retagged them. It agreed with 75% of the previous tags. Jev found about 579 high-confidence topic changes. I evaluated reliability by manually labeling 30 disagreements and accepted all of them. I was astonished by Jev's classification capabilities. We applied and verified all changes in production. I'm much more satisfied with the classifications, but I think there is still room for improvement. Check out the papers here: I will experiment with Jev more. The takeaway is that pipelines can be significantly improved by carefully combining System One and System Two models. Jev clearly unlocks more interesting ways to organize papers and offer a more useful discovery layer for research papers. More updates on that soon.

elvis

15,427 次观看 • 8 天前

If you need McKinsey-style slides, try this prompt: I asked Kimi to conduct a comprehensive analysis of the GenAI video model market, focusing on leading players (e.g., Seedance 2.0, Sora, Kling, Veo, Luma). My prompt: Conduct a comprehensive analysis of the GenAI video model market, focusing on leading players (e.g., Seedance 2.0, Kling, Veo, Luma). Compare their core architectures, temporal consistency, and prompt adherence to identify current industry benchmarks. Use the latest information Requirement: A professional, high-density consulting presentation slide, designed in the style of a top-tier strategy firm (McKinsey/BCG) blended with high-end editorial aesthetics. Core Content & Layout: 1. Rich Data Visualization: The slide is populated with complex, precise charts (stacked bar charts, waterfall charts, or line graphs) and detailed data tables with rows and columns. 2. Structured Frameworks: Includes strategic diagrams or 2x2 matrices constructed with thin, clean lines. 3. High Information Density: The layout is sophisticated and multi-column, mimicking an actual business analysis deck, not just an empty cover page. Visual Style: 1. Aesthetic: Tech-minimalist but information-heavy. Clean, sharp, and authoritative. 2. Typography: Serif fonts (like Times New Roman) for the main headlines to give a premium financial report feel; clean Sans-serif for chart labels and data numbers. 3. Color Palette: Clean white background. Text is sharp black. Charts and graphical accents use Deep Royal Blue and distinct shades of grey for data hierarchy. 4. Graphics: Use fine hairline borders for tables and precise vector lines for graphs.

If you need McKinsey-style slides, try this prompt: I asked Kimi to conduct a comprehensive analysis of the GenAI video model market, focusing on leading players (e.g., Seedance 2.0, Sora, Kling, Veo, Luma). My prompt: Conduct a comprehensive analysis of the GenAI video model market, focusing on leading players (e.g., Seedance 2.0, Kling, Veo, Luma). Compare their core architectures, temporal consistency, and prompt adherence to identify current industry benchmarks. Use the latest information Requirement: A professional, high-density consulting presentation slide, designed in the style of a top-tier strategy firm (McKinsey/BCG) blended with high-end editorial aesthetics. Core Content & Layout: 1. Rich Data Visualization: The slide is populated with complex, precise charts (stacked bar charts, waterfall charts, or line graphs) and detailed data tables with rows and columns. 2. Structured Frameworks: Includes strategic diagrams or 2x2 matrices constructed with thin, clean lines. 3. High Information Density: The layout is sophisticated and multi-column, mimicking an actual business analysis deck, not just an empty cover page. Visual Style: 1. Aesthetic: Tech-minimalist but information-heavy. Clean, sharp, and authoritative. 2. Typography: Serif fonts (like Times New Roman) for the main headlines to give a premium financial report feel; clean Sans-serif for chart labels and data numbers. 3. Color Palette: Clean white background. Text is sharp black. Charts and graphical accents use Deep Royal Blue and distinct shades of grey for data hierarchy. 4. Graphics: Use fine hairline borders for tables and precise vector lines for graphs.

Crystal

707,951 次观看 • 7 个月前

Conducting road safety audits, scheduling maintenance, or planning logistics routes used to require combing through imagery or, worse, driving miles of roads yourself. Not anymore. New AI-powered data layers in Google Earth are changing that. Pulling from billions of Google Street View images, these layers now spot infrastructure assets like stop signs, speed limit signs, and more to map infrastructure for you. By combining these assets into a single project and diving into Street View you can locate and validate assets on Google Earth in seconds. These layers are now available for Professional & Professional Advanced customers on web and Android, with coverage expanding over the coming weeks.

Conducting road safety audits, scheduling maintenance, or planning logistics routes used to require combing through imagery or, worse, driving miles of roads yourself. Not anymore. New AI-powered data layers in Google Earth are changing that. Pulling from billions of Google Street View images, these layers now spot infrastructure assets like stop signs, speed limit signs, and more to map infrastructure for you. By combining these assets into a single project and diving into Street View you can locate and validate assets on Google Earth in seconds. These layers are now available for Professional & Professional Advanced customers on web and Android, with coverage expanding over the coming weeks.

Google Earth

21,270 次观看 • 3 个月前

🫡😠Apple takes a dark turn in the EU when the DMA forced them to open iOS to everyone. Instead of walking in the shoes that developers have for years (14+ for us) and realizing they have made tech restrictions so harsh that it’s made anyone outside of the “NC-17” rated based AppStore harmed and a “outsider”. Now, they have eliminated the one option we all had.. PWA/web apps (save to home screen) apps. They didn’t stop there.. they went further, now they expect to be paid fees and more and for developers to keep an account for all installs and usage. Not to worry guys, we have already planned ahead for this for months now. We will continue to develop and grow our apps (more are coming). My goal has always been to create the best experience we all deserve. Now we have more options that they can’t control completely. It’s just a lot of work towards what we had for over a decade now has to be rewritten and changed. 🧑🏻‍💻😳🙄🥱😒🧐🤓 - PiGBoss (full article here):

🫡😠Apple takes a dark turn in the EU when the DMA forced them to open iOS to everyone. Instead of walking in the shoes that developers have for years (14+ for us) and realizing they have made tech restrictions so harsh that it’s made anyone outside of the “NC-17” rated based AppStore harmed and a “outsider”. Now, they have eliminated the one option we all had.. PWA/web apps (save to home screen) apps. They didn’t stop there.. they went further, now they expect to be paid fees and more and for developers to keep an account for all installs and usage. Not to worry guys, we have already planned ahead for this for months now. We will continue to develop and grow our apps (more are coming). My goal has always been to create the best experience we all deserve. Now we have more options that they can’t control completely. It’s just a lot of work towards what we had for over a decade now has to be rewritten and changed. 🧑🏻‍💻😳🙄🥱😒🧐🤓 - PiGBoss (full article here):

NastyKinkPigs.com

15,783 次观看 • 2 年前