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Ring lets police ask for security videos. Here’s how to opt out

Ring lets police ask for security videos. Here’s how to opt out

The police can’t just come and take your Ring security videos without a warrant, but they can ask for your captured video clips, all thanks for a feature in Ring’s Neighbors app. That feature is called Community Requests, and it allows law enforcement officials to request Ring camera footage they believe might help them in their investigations. For its part, Ring calls Community Requests a “completely voluntary” tool for “empowering our communities to play a role in public safety,” as well as an “important aspect” of its “mission of making communities safer.” But the functionality has drawn fierce criticism from privacy advocates , who argue that the program (a newer and reworked incarnation of Ring’s earlier Request for Assistance effort ) represents Ring’s “most invasive feature.” Just this week, Community Requests have come under renewed scrutiny following word of Ring’s partnership with Flock , a network of AI-powered security cameras that (among other things) can scan vehicle license plates while also allowing police to search camera footage via natural-language queries, according to TechCrunch . How Ring’s Community Requests feature works When police send requests through the program, the details will appear in the public feed of Ring’s Neighbors app, and you’ll get a notification if your Ring camera was in the vicinity of a given incident. If—and only if—you choose to help, you can select and upload individual Ring videos to investigators. Just be aware that if you do, your home address and email will be shared along with the video clips, and submitted videos can’t be retracted. On the flip side, Ring promises that “no information is shared” about who viewed or ignored a Community Request, and that your camera footage is “not automatically shared with law enforcement.” How to opt out of Community Requests If you want to opt out of Community Requests entirely, there’s an easy way to do so. Ben Patterson/Foundry Open Ring’s Neighbor’s app, open the main menu (just tap the three-line “hamburger” button in the top-left corner of the screen), then tap Neighborhood Settings > Feed Settings . Uncheck the box next to Community Requests . Tap the Apply button. Once that’s done, you won’t see Community Requests from law enforcement in your Neighbors feed anymore. This article is part of TechHive’s in-depth coverage of the best security cameras .

Ring lets police ask for security videos. Here’s how to opt out

Ring lets police ask for security videos. Here’s how to opt out

The police can’t just come and take your Ring security videos without a warrant, but they can ask for your captured video clips, all thanks for a feature in Ring’s Neighbors app. That feature is called Community Requests, and it allows law enforcement officials to request Ring camera footage they believe might help them in their investigations. For its part, Ring calls Community Requests a “completely voluntary” tool for “empowering our communities to play a role in public safety,” as well as an “important aspect” of its “mission of making communities safer.” But the functionality has drawn fierce criticism from privacy advocates , who argue that the program (a newer and reworked incarnation of Ring’s earlier Request for Assistance effort ) represents Ring’s “most invasive feature.” Just this week, Community Requests have come under renewed scrutiny following word of Ring’s partnership with Flock , a network of AI-powered security cameras that (among other things) can scan vehicle license plates while also allowing police to search camera footage via natural-language queries, according to TechCrunch . How Ring’s Community Requests feature works When police send requests through the program, the details will appear in the public feed of Ring’s Neighbors app, and you’ll get a notification if your Ring camera was in the vicinity of a given incident. If—and only if—you choose to help, you can select and upload individual Ring videos to investigators. Just be aware that if you do, your home address and email will be shared along with the video clips, and submitted videos can’t be retracted. On the flip side, Ring promises that “no information is shared” about who viewed or ignored a Community Request, and that your camera footage is “not automatically shared with law enforcement.” How to opt out of Community Requests If you want to opt out of Community Requests entirely, there’s an easy way to do so. Ben Patterson/Foundry Open Ring’s Neighbor’s app, open the main menu (just tap the three-line “hamburger” button in the top-left corner of the screen), then tap Neighborhood Settings > Feed Settings . Uncheck the box next to Community Requests . Tap the Apply button. Once that’s done, you won’t see Community Requests from law enforcement in your Neighbors feed anymore. This article is part of TechHive’s in-depth coverage of the best security cameras .

Ring lets police ask for security videos. Here’s how to opt out

Ring lets police ask for security videos. Here’s how to opt out

The police can’t just come and take your Ring security videos without a warrant, but they can ask for your captured video clips, all thanks for a feature in Ring’s Neighbors app. That feature is called Community Requests, and it allows law enforcement officials to request Ring camera footage they believe might help them in their investigations. For its part, Ring calls Community Requests a “completely voluntary” tool for “empowering our communities to play a role in public safety,” as well as an “important aspect” of its “mission of making communities safer.” But the functionality has drawn fierce criticism from privacy advocates , who argue that the program (a newer and reworked incarnation of Ring’s earlier Request for Assistance effort ) represents Ring’s “most invasive feature.” Just this week, Community Requests have come under renewed scrutiny following word of Ring’s partnership with Flock , a network of AI-powered security cameras that (among other things) can scan vehicle license plates while also allowing police to search camera footage via natural-language queries, according to TechCrunch . How Ring’s Community Requests feature works When police send requests through the program, the details will appear in the public feed of Ring’s Neighbors app, and you’ll get a notification if your Ring camera was in the vicinity of a given incident. If—and only if—you choose to help, you can select and upload individual Ring videos to investigators. Just be aware that if you do, your home address and email will be shared along with the video clips, and submitted videos can’t be retracted. On the flip side, Ring promises that “no information is shared” about who viewed or ignored a Community Request, and that your camera footage is “not automatically shared with law enforcement.” How to opt out of Community Requests If you want to opt out of Community Requests entirely, there’s an easy way to do so. Ben Patterson/Foundry Open Ring’s Neighbor’s app, open the main menu (just tap the three-line “hamburger” button in the top-left corner of the screen), then tap Neighborhood Settings > Feed Settings . Uncheck the box next to Community Requests . Tap the Apply button. Once that’s done, you won’t see Community Requests from law enforcement in your Neighbors feed anymore. This article is part of TechHive’s in-depth coverage of the best security cameras .

Ring lets police ask for security videos. Here’s how to opt out

Ring lets police ask for security videos. Here’s how to opt out

The police can’t just come and take your Ring security videos without a warrant, but they can ask for your captured video clips, all thanks for a feature in Ring’s Neighbors app. That feature is called Community Requests, and it allows law enforcement officials to request Ring camera footage they believe might help them in their investigations. For its part, Ring calls Community Requests a “completely voluntary” tool for “empowering our communities to play a role in public safety,” as well as an “important aspect” of its “mission of making communities safer.” But the functionality has drawn fierce criticism from privacy advocates , who argue that the program (a newer and reworked incarnation of Ring’s earlier Request for Assistance effort ) represents Ring’s “most invasive feature.” Just this week, Community Requests have come under renewed scrutiny following word of Ring’s partnership with Flock , a network of AI-powered security cameras that (among other things) can scan vehicle license plates while also allowing police to search camera footage via natural-language queries, according to TechCrunch . How Ring’s Community Requests feature works When police send requests through the program, the details will appear in the public feed of Ring’s Neighbors app, and you’ll get a notification if your Ring camera was in the vicinity of a given incident. If—and only if—you choose to help, you can select and upload individual Ring videos to investigators. Just be aware that if you do, your home address and email will be shared along with the video clips, and submitted videos can’t be retracted. On the flip side, Ring promises that “no information is shared” about who viewed or ignored a Community Request, and that your camera footage is “not automatically shared with law enforcement.” How to opt out of Community Requests If you want to opt out of Community Requests entirely, there’s an easy way to do so. Ben Patterson/Foundry Open Ring’s Neighbor’s app, open the main menu (just tap the three-line “hamburger” button in the top-left corner of the screen), then tap Neighborhood Settings > Feed Settings . Uncheck the box next to Community Requests . Tap the Apply button. Once that’s done, you won’t see Community Requests from law enforcement in your Neighbors feed anymore. This article is part of TechHive’s in-depth coverage of the best security cameras .

Codev lets enterprises avoid vibe coding hangovers with a team of agents that generate and document code

Codev lets enterprises avoid vibe coding hangovers with a team of agents that generate and document code

For many software developers using generative AI, vibe coding is a double-edged sword. The process delivers rapid prototypes but often leaves a trail of brittle, undocumented code that creates significant technical debt. A new open-source platform, Codev , addresses this by proposing a fundamental shift: treating the natural language conversation with an AI as part of the actual source code. Codev is based on SP(IDE)R, a framework designed to turn vibe-coding conversations into structured, versioned, and auditable assets that become part of the code repository. What is Codev? At its core, Codev is a methodology that treats natural language context as an integral part of the development lifecycle as opposed to a disposable artifact as is the case with vanilla vibe coding. According to co-founder Waleed Kadous, the goal is to invert the typical engineering workflow. "A key principle of Codev is that documents like the specification are the actual code of the system," he told VentureBeat. "It's almost like natural language is compiled down into Typescript by our agents." This approach avoids the common pitfall where documentation is created after the fact, if at all. Its flagship protocol, SP(IDE)R, provides a lightweight but formal structure for building software. The process begins with Specify , where a human and multiple AI agents collaborate to turn a high-level request into concrete acceptance criteria. Next, in the Plan stage, an AI proposes a phased implementation, which is again reviewed. For each phase, the AI enters an IDE loop : it Implements the code, Defends it against bugs and regression with comprehensive tests, and Evaluates the result against the specification. The final step is Review , where the team documents lessons learned to update and improve the SP(IDE)R protocol itself for future projects. The framework’s key differentiator is its use of multiple agents and explicit human review at different stages. Kadous notes that each agent brings unique strengths to the review process. "Gemini is extremely good at catching security issues," he said, citing a critical cross-site scripting (XSS) flaw and another bug that "would have shared an OpenAI API key with the client, which could cost thousands of dollars." Meanwhile, "GPT-5 is very good at understanding how to simplify a design." This structured review, with a human providing final approval at each stage, prevents the kind of runaway automation that leads to flawed code. The platform’s AI-native philosophy extends to its installation. There is no complex installer; instead, a user instructs their AI agent to apply the Codev GitHub repository to set up the project. The developers "dogfooded" their framework, using Codev to build Codev. “The key point here is that natural language is executable now, with the agent being the interpreter,” Kadous said. “This is great because it means it's not a ‘blind’ integration of Codev, the agent gets to choose the best way to integrate it and can intelligently make decisions.” Codev case study To test the framework's effectiveness, its creators ran a direct comparison between vanilla vibe-coding and Codev. They gave Claude Opus 4.1 a request to build a modern web-based todo manager. The first attempt used a conversational, vibe-coding approach. The result was a plausible-looking demo. However, an automated analysis conducted by three independent AI agents found that it had implemented 0% of the required functionality, contained no tests, and lacked a database or API. The second attempt used the same AI model and prompt but applied the SP(IDE)R protocol. This time, the AI produced a production-ready application with 32 source files, 100% of the specified functionality, five test suites, a SQLite database, and a complete RESTful API. Throughout this process, the human developers reported they never directly edited a single line of source code. While this was a single experiment, Kadous estimates the impact is substantial. "Subjectively, it feels like I'm about three times as productive with Codev as without," he says. The quality also speaks for itself. "I used LLMs as a judge, and one of them described the output like what a well-oiled engineering team would produce. That was exactly what I was aiming for." While the process is powerful, it redefines the developer's role from a hands-on coder to a system architect and reviewer. According to Kadous, the initial spec and plan stages can each take between 45 minutes to two hours of focused collaboration. This is in contrast to the impression given by many vibe-coding platforms, where a single prompt and a few minutes of processing gives you a fully functional and scalable application. "All of the value I add is in the background knowledge I apply to the specs and plans," he explains. He emphasizes that the framework is designed to augment, not replace, experienced talent. "The people who will do the best... are senior engineers and above because they know the pitfalls... It just takes the senior engineer you already have and makes them much more productive." A future of human and AI collaboration Frameworks like Codev signal a shift where the primary creative act of software development moves from writing code to crafting precise, machine-readable specifications and plans. For enterprise teams, this means AI-generated code can become auditable, maintainable, and reliable. By capturing the entire development conversation in version control and enforcing it with CI, the process turns ephemeral chats into durable engineering assets. Codev proposes a future where the AI acts not as a chaotic assistant, but as a disciplined collaborator in a structured, human-led workflow. However, Kadous acknowledges this shift creates new challenges for the workforce. "Senior engineers that reject AI outright will be outpaced by senior engineers who embrace it," he predicts. He also expresses concern for junior developers who may not get the chance "to build their architectural chops," a skill that becomes even more critical when guiding AI. This highlights a central challenge for the industry: ensuring that as AI elevates top performers, it also creates pathways to develop the next generation of talent.

Codev lets enterprises avoid vibe coding hangovers with a team of agents that generate and document code

Codev lets enterprises avoid vibe coding hangovers with a team of agents that generate and document code

For many software developers using generative AI, vibe coding is a double-edged sword. The process delivers rapid prototypes but often leaves a trail of brittle, undocumented code that creates significant technical debt. A new open-source platform, Codev , addresses this by proposing a fundamental shift: treating the natural language conversation with an AI as part of the actual source code. Codev is based on SP(IDE)R, a framework designed to turn vibe-coding conversations into structured, versioned, and auditable assets that become part of the code repository. What is Codev? At its core, Codev is a methodology that treats natural language context as an integral part of the development lifecycle as opposed to a disposable artifact as is the case with vanilla vibe coding. According to co-founder Waleed Kadous, the goal is to invert the typical engineering workflow. "A key principle of Codev is that documents like the specification are the actual code of the system," he told VentureBeat. "It's almost like natural language is compiled down into Typescript by our agents." This approach avoids the common pitfall where documentation is created after the fact, if at all. Its flagship protocol, SP(IDE)R, provides a lightweight but formal structure for building software. The process begins with Specify , where a human and multiple AI agents collaborate to turn a high-level request into concrete acceptance criteria. Next, in the Plan stage, an AI proposes a phased implementation, which is again reviewed. For each phase, the AI enters an IDE loop : it Implements the code, Defends it against bugs and regression with comprehensive tests, and Evaluates the result against the specification. The final step is Review , where the team documents lessons learned to update and improve the SP(IDE)R protocol itself for future projects. The framework’s key differentiator is its use of multiple agents and explicit human review at different stages. Kadous notes that each agent brings unique strengths to the review process. "Gemini is extremely good at catching security issues," he said, citing a critical cross-site scripting (XSS) flaw and another bug that "would have shared an OpenAI API key with the client, which could cost thousands of dollars." Meanwhile, "GPT-5 is very good at understanding how to simplify a design." This structured review, with a human providing final approval at each stage, prevents the kind of runaway automation that leads to flawed code. The platform’s AI-native philosophy extends to its installation. There is no complex installer; instead, a user instructs their AI agent to apply the Codev GitHub repository to set up the project. The developers "dogfooded" their framework, using Codev to build Codev. “The key point here is that natural language is executable now, with the agent being the interpreter,” Kadous said. “This is great because it means it's not a ‘blind’ integration of Codev, the agent gets to choose the best way to integrate it and can intelligently make decisions.” Codev case study To test the framework's effectiveness, its creators ran a direct comparison between vanilla vibe-coding and Codev. They gave Claude Opus 4.1 a request to build a modern web-based todo manager. The first attempt used a conversational, vibe-coding approach. The result was a plausible-looking demo. However, an automated analysis conducted by three independent AI agents found that it had implemented 0% of the required functionality, contained no tests, and lacked a database or API. The second attempt used the same AI model and prompt but applied the SP(IDE)R protocol. This time, the AI produced a production-ready application with 32 source files, 100% of the specified functionality, five test suites, a SQLite database, and a complete RESTful API. Throughout this process, the human developers reported they never directly edited a single line of source code. While this was a single experiment, Kadous estimates the impact is substantial. "Subjectively, it feels like I'm about three times as productive with Codev as without," he says. The quality also speaks for itself. "I used LLMs as a judge, and one of them described the output like what a well-oiled engineering team would produce. That was exactly what I was aiming for." While the process is powerful, it redefines the developer's role from a hands-on coder to a system architect and reviewer. According to Kadous, the initial spec and plan stages can each take between 45 minutes to two hours of focused collaboration. This is in contrast to the impression given by many vibe-coding platforms, where a single prompt and a few minutes of processing gives you a fully functional and scalable application. "All of the value I add is in the background knowledge I apply to the specs and plans," he explains. He emphasizes that the framework is designed to augment, not replace, experienced talent. "The people who will do the best... are senior engineers and above because they know the pitfalls... It just takes the senior engineer you already have and makes them much more productive." A future of human and AI collaboration Frameworks like Codev signal a shift where the primary creative act of software development moves from writing code to crafting precise, machine-readable specifications and plans. For enterprise teams, this means AI-generated code can become auditable, maintainable, and reliable. By capturing the entire development conversation in version control and enforcing it with CI, the process turns ephemeral chats into durable engineering assets. Codev proposes a future where the AI acts not as a chaotic assistant, but as a disciplined collaborator in a structured, human-led workflow. However, Kadous acknowledges this shift creates new challenges for the workforce. "Senior engineers that reject AI outright will be outpaced by senior engineers who embrace it," he predicts. He also expresses concern for junior developers who may not get the chance "to build their architectural chops," a skill that becomes even more critical when guiding AI. This highlights a central challenge for the industry: ensuring that as AI elevates top performers, it also creates pathways to develop the next generation of talent.

Codev lets enterprises avoid vibe coding hangovers with a team of agents that generate and document code

Codev lets enterprises avoid vibe coding hangovers with a team of agents that generate and document code

For many software developers using generative AI, vibe coding is a double-edged sword. The process delivers rapid prototypes but often leaves a trail of brittle, undocumented code that creates significant technical debt. A new open-source platform, Codev , addresses this by proposing a fundamental shift: treating the natural language conversation with an AI as part of the actual source code. Codev is based on SP(IDE)R, a framework designed to turn vibe-coding conversations into structured, versioned, and auditable assets that become part of the code repository. What is Codev? At its core, Codev is a methodology that treats natural language context as an integral part of the development lifecycle as opposed to a disposable artifact as is the case with vanilla vibe coding. According to co-founder Waleed Kadous, the goal is to invert the typical engineering workflow. "A key principle of Codev is that documents like the specification are the actual code of the system," he told VentureBeat. "It's almost like natural language is compiled down into Typescript by our agents." This approach avoids the common pitfall where documentation is created after the fact, if at all. Its flagship protocol, SP(IDE)R, provides a lightweight but formal structure for building software. The process begins with Specify , where a human and multiple AI agents collaborate to turn a high-level request into concrete acceptance criteria. Next, in the Plan stage, an AI proposes a phased implementation, which is again reviewed. For each phase, the AI enters an IDE loop : it Implements the code, Defends it against bugs and regression with comprehensive tests, and Evaluates the result against the specification. The final step is Review , where the team documents lessons learned to update and improve the SP(IDE)R protocol itself for future projects. The framework’s key differentiator is its use of multiple agents and explicit human review at different stages. Kadous notes that each agent brings unique strengths to the review process. "Gemini is extremely good at catching security issues," he said, citing a critical cross-site scripting (XSS) flaw and another bug that "would have shared an OpenAI API key with the client, which could cost thousands of dollars." Meanwhile, "GPT-5 is very good at understanding how to simplify a design." This structured review, with a human providing final approval at each stage, prevents the kind of runaway automation that leads to flawed code. The platform’s AI-native philosophy extends to its installation. There is no complex installer; instead, a user instructs their AI agent to apply the Codev GitHub repository to set up the project. The developers "dogfooded" their framework, using Codev to build Codev. “The key point here is that natural language is executable now, with the agent being the interpreter,” Kadous said. “This is great because it means it's not a ‘blind’ integration of Codev, the agent gets to choose the best way to integrate it and can intelligently make decisions.” Codev case study To test the framework's effectiveness, its creators ran a direct comparison between vanilla vibe-coding and Codev. They gave Claude Opus 4.1 a request to build a modern web-based todo manager. The first attempt used a conversational, vibe-coding approach. The result was a plausible-looking demo. However, an automated analysis conducted by three independent AI agents found that it had implemented 0% of the required functionality, contained no tests, and lacked a database or API. The second attempt used the same AI model and prompt but applied the SP(IDE)R protocol. This time, the AI produced a production-ready application with 32 source files, 100% of the specified functionality, five test suites, a SQLite database, and a complete RESTful API. Throughout this process, the human developers reported they never directly edited a single line of source code. While this was a single experiment, Kadous estimates the impact is substantial. "Subjectively, it feels like I'm about three times as productive with Codev as without," he says. The quality also speaks for itself. "I used LLMs as a judge, and one of them described the output like what a well-oiled engineering team would produce. That was exactly what I was aiming for." While the process is powerful, it redefines the developer's role from a hands-on coder to a system architect and reviewer. According to Kadous, the initial spec and plan stages can each take between 45 minutes to two hours of focused collaboration. This is in contrast to the impression given by many vibe-coding platforms, where a single prompt and a few minutes of processing gives you a fully functional and scalable application. "All of the value I add is in the background knowledge I apply to the specs and plans," he explains. He emphasizes that the framework is designed to augment, not replace, experienced talent. "The people who will do the best... are senior engineers and above because they know the pitfalls... It just takes the senior engineer you already have and makes them much more productive." A future of human and AI collaboration Frameworks like Codev signal a shift where the primary creative act of software development moves from writing code to crafting precise, machine-readable specifications and plans. For enterprise teams, this means AI-generated code can become auditable, maintainable, and reliable. By capturing the entire development conversation in version control and enforcing it with CI, the process turns ephemeral chats into durable engineering assets. Codev proposes a future where the AI acts not as a chaotic assistant, but as a disciplined collaborator in a structured, human-led workflow. However, Kadous acknowledges this shift creates new challenges for the workforce. "Senior engineers that reject AI outright will be outpaced by senior engineers who embrace it," he predicts. He also expresses concern for junior developers who may not get the chance "to build their architectural chops," a skill that becomes even more critical when guiding AI. This highlights a central challenge for the industry: ensuring that as AI elevates top performers, it also creates pathways to develop the next generation of talent.

Codev lets enterprises avoid vibe coding hangovers with a team of agents that generate and document code

Codev lets enterprises avoid vibe coding hangovers with a team of agents that generate and document code

For many software developers using generative AI, vibe coding is a double-edged sword. The process delivers rapid prototypes but often leaves a trail of brittle, undocumented code that creates significant technical debt. A new open-source platform, Codev , addresses this by proposing a fundamental shift: treating the natural language conversation with an AI as part of the actual source code. Codev is based on SP(IDE)R, a framework designed to turn vibe-coding conversations into structured, versioned, and auditable assets that become part of the code repository. What is Codev? At its core, Codev is a methodology that treats natural language context as an integral part of the development lifecycle as opposed to a disposable artifact as is the case with vanilla vibe coding. According to co-founder Waleed Kadous, the goal is to invert the typical engineering workflow. "A key principle of Codev is that documents like the specification are the actual code of the system," he told VentureBeat. "It's almost like natural language is compiled down into Typescript by our agents." This approach avoids the common pitfall where documentation is created after the fact, if at all. Its flagship protocol, SP(IDE)R, provides a lightweight but formal structure for building software. The process begins with Specify , where a human and multiple AI agents collaborate to turn a high-level request into concrete acceptance criteria. Next, in the Plan stage, an AI proposes a phased implementation, which is again reviewed. For each phase, the AI enters an IDE loop : it Implements the code, Defends it against bugs and regression with comprehensive tests, and Evaluates the result against the specification. The final step is Review , where the team documents lessons learned to update and improve the SP(IDE)R protocol itself for future projects. The framework’s key differentiator is its use of multiple agents and explicit human review at different stages. Kadous notes that each agent brings unique strengths to the review process. "Gemini is extremely good at catching security issues," he said, citing a critical cross-site scripting (XSS) flaw and another bug that "would have shared an OpenAI API key with the client, which could cost thousands of dollars." Meanwhile, "GPT-5 is very good at understanding how to simplify a design." This structured review, with a human providing final approval at each stage, prevents the kind of runaway automation that leads to flawed code. The platform’s AI-native philosophy extends to its installation. There is no complex installer; instead, a user instructs their AI agent to apply the Codev GitHub repository to set up the project. The developers "dogfooded" their framework, using Codev to build Codev. “The key point here is that natural language is executable now, with the agent being the interpreter,” Kadous said. “This is great because it means it's not a ‘blind’ integration of Codev, the agent gets to choose the best way to integrate it and can intelligently make decisions.” Codev case study To test the framework's effectiveness, its creators ran a direct comparison between vanilla vibe-coding and Codev. They gave Claude Opus 4.1 a request to build a modern web-based todo manager. The first attempt used a conversational, vibe-coding approach. The result was a plausible-looking demo. However, an automated analysis conducted by three independent AI agents found that it had implemented 0% of the required functionality, contained no tests, and lacked a database or API. The second attempt used the same AI model and prompt but applied the SP(IDE)R protocol. This time, the AI produced a production-ready application with 32 source files, 100% of the specified functionality, five test suites, a SQLite database, and a complete RESTful API. Throughout this process, the human developers reported they never directly edited a single line of source code. While this was a single experiment, Kadous estimates the impact is substantial. "Subjectively, it feels like I'm about three times as productive with Codev as without," he says. The quality also speaks for itself. "I used LLMs as a judge, and one of them described the output like what a well-oiled engineering team would produce. That was exactly what I was aiming for." While the process is powerful, it redefines the developer's role from a hands-on coder to a system architect and reviewer. According to Kadous, the initial spec and plan stages can each take between 45 minutes to two hours of focused collaboration. This is in contrast to the impression given by many vibe-coding platforms, where a single prompt and a few minutes of processing gives you a fully functional and scalable application. "All of the value I add is in the background knowledge I apply to the specs and plans," he explains. He emphasizes that the framework is designed to augment, not replace, experienced talent. "The people who will do the best... are senior engineers and above because they know the pitfalls... It just takes the senior engineer you already have and makes them much more productive." A future of human and AI collaboration Frameworks like Codev signal a shift where the primary creative act of software development moves from writing code to crafting precise, machine-readable specifications and plans. For enterprise teams, this means AI-generated code can become auditable, maintainable, and reliable. By capturing the entire development conversation in version control and enforcing it with CI, the process turns ephemeral chats into durable engineering assets. Codev proposes a future where the AI acts not as a chaotic assistant, but as a disciplined collaborator in a structured, human-led workflow. However, Kadous acknowledges this shift creates new challenges for the workforce. "Senior engineers that reject AI outright will be outpaced by senior engineers who embrace it," he predicts. He also expresses concern for junior developers who may not get the chance "to build their architectural chops," a skill that becomes even more critical when guiding AI. This highlights a central challenge for the industry: ensuring that as AI elevates top performers, it also creates pathways to develop the next generation of talent.