#how to access devin ai software engineer

[webideasolutionca]

Explore the groundbreaking journey of Devin, the world's premier AI software engineer, on Web Idea Solution. Discover how Devin's innovative approach revolutionizes development, marking a significant milestone in the intersection of artificial intelligence and software engineering. Dive into the transformative potential of AI-driven programming and its implications for the future of technology.

[govindhtech]

Devin AI: The World’s First AI Software Engineer vs ChatGPT

You may be asking which of ChatGPT vs. Devin AI is superior in the constantly evolving field of artificial intelligence, where a new game-changer is introduced every week. ChatGPT is without a doubt the industry leader at the moment. And we’ll attempt to address it as thoroughly as possible.

ChatGPT is the most popular AI tool with 189 million users. Since its November 2022 mainstreaming, it has dominated AI discussions. Devin AI, in contrast, has only been operational for a short while. With $21 million in venture finance from Peter Thiel’s Founders Fund, it emerged from stealth.

What distinguishes Devin AI from other AI systems? Devin’s versatility and independence in a variety of jobs are its USPs. It demonstrates notable breakthroughs in AI technology, from system debugging to code contribution to open-source projects. In one instance, Devin’s developers had it accept a job offer from Upwork to improve a computer vision system, do the assignment, and be paid all without the need for human help. Devin looks to manuals for answers when confronted with problems; although it’s not yet ready to take the position of more experienced software engineers, it already demonstrates the abilities of a highly skilled junior developer.

Having early access to Devin, Andrew Kean Gao has written a thorough X thread summarizing his experiences. It displays some of its drawbacks as well as its potential. Devin is the creator of the fully functional Chrome extension Gao and has put in 80% of the effort on a map showing the sea temperatures in Antarctica over the last 50 years. It’s not flawless for instance, it was unable to complete the development of a chess website that allows users to compete against LLMs but it does represent a significant step toward the development of agent-based AIs.

AI-assisted coding is a feature of both tools, thus it’s important to compare them and determine their similarities and differences. Let’s look into it and see whether Devin AI, the challenger, has what it takes to unseat the present monarch!

Devin AI vs. ChatGPT: Emergence This week, Devin, the newbie from Cognition AI, has garnered a lot of attention. It was developed by a coding team that includes brothers Scott and Andrew Wu, who have been winning international coding contests since they were in their teens, and it is expected to raise the bar for software creation with AI assistance.

Conversely, ChatGPT is the industry leader and innovator. a cutting-edge, adaptable large language model (LLM) with shown performance in speech-assisted querying, writing, coding, and imaging. Right now, every other AI software is evaluated against it as a standard.

Devin AI vs ChatGPT – Coding Devin’s Ability to Code Devin’s degree of independence surpasses ChatGPT’s conversational capabilities, which need human involvement. Rather, Devin’s provides a unique feature: the capacity to organize, carry out, and cooperate on difficult software engineering assignments. It has been dubbed “The World’s First AI Software Engineer” for its creative ability. Devin seems to be unmatched in this area. While there are many AI tools available to assist with coding, Devin has the potential to usher in a new age of AI coding if it lives up to the promise.

Three Illustrations of Devin’s Mastery in Coding: Devin Creates Pictures With Text Hidden One of Devin’s engineers, Sara, shows off the tool’s capacity to insert a secret text message into a picture in below video. She gives Devin the picture resolution (1080p), asks Devin to hide her name in the concealed text, and links him to an appropriate blog page that demonstrates how to do this. She then lets Devin finish the assignment.

Devin Eliminates Software Errors A Cognition engineer named Neil gives Devin instructions on how to resolve an issue on a GitHub repository in this video. He gives Devin the link, describes the issue, and lets Devin figure it out.

Devin is an Upwork freelancer Devin became popular on social media as a result of this video. Walden, a cognitive engineer, talks on how Devin finished a task on Upwork. The assignment was to configure a computer vision system. Devin understood, corrected, and updated the client’s code, among other things, and dealt with them all. After that, Devin provided example photos and a final file outlining how the work had been accomplished.

Use of ChatGPT for Coding Though its capabilities as a general-purpose LLM are well known, ChatGPT is also a rather intelligent code generator. An overview of its coding skills is provided below:

Code Generation: In response to user commands, ChatGPT may produce code snippets in a variety of programming languages. It’s a very helpful tool for many common programming difficulties and for prototyping.

Bug Fixing: OpenAI’s LLM can help with issue identification, solution outlines, and bug fix recommendations.

Code Explainer: ChatGPT is an extremely helpful tool for anybody learning to program because of how well it generates and explains code snippets.

Advice & Suggestions: Practical recommendations, counsel, and detailed directions for finishing ordinary code. The ChatGPT is a great development companion since it has your back.

Independence Devin works in a sandboxed environment that is similar to the workplace of a developer. It has access to standard development tools inside that sandbox, including a code editor, a browser, and the shell. Within such setting, it is capable of autonomous planning and execution. It continues the useful practice of providing the user with updates in a chat window about its progress while it’s doing this.

On the other hand, ChatGPT offers a conversational interface that requires users to continually provide voice commands or text prompts. ChatGPT operates outside of a visible development environment, even though it explains what it’s doing and complies with user requests to write code.

Real-time cooperation

Devin AI’s skills as a model particularly built to assist with software development are shown by its capacity to cooperate in real-time, take input, and modify its behaviors appropriately. It enables developers to collaborate with Devin as if they were human teammates. A developer may allow Devin to do tasks on its own since it exhibits such a high degree of autonomy and knows it will only need minimal input and course correction.

As we explain in our ChatGPT review, ChatGPT, on the other hand, lacks expertise in software development but may iteratively improve its results depending on user feedback. It has to communicate back and forth with the user in order to proceed. It cannot function autonomously as a consequence. Although it may and does provide helpful support, it cannot—as of yet—be regarded as collaborative in the same sense as Devin.

Devin AI vs. ChatGPT: Learning Devin AI and ChatGPT are both capable of picking up and using new knowledge. Because Devin was created with software engineering in mind, he can build and debug software, learn new technologies, read manuals, search the Internet for pertinent information, and contribute to code repositories. This independence and capacity for quick problem-solving constitute a very noteworthy advancement in this field.

While ChatGPT may learn new topics, its learning process is mostly focused on using its extensive corpus of taught information as an LLM. This is either the result of human input (pasting an article, uploading a picture or PDF, or fine-tuning on a particular dataset). Devin is more knowledgeable than ChatGPT, but ChatGPT is more passive. It doesn’t look for fresh information on its own or pick up knowledge from encounters in real time. In spite of this, it demonstrates an extraordinarily thorough comprehension of a wide range of topics and is a priceless learning aid and specialist resource.

Adoption and Accessibility ChatGPT’s large and expanding user base has already changed AI and the globe. This product is already one of history’s most important. Its technology has found broad use, and the OpenAI’s partnership with Microsoft is driving the company’s expansion.

Conversely, Devin AI remains under limited access; the only information available about it is from demonstrations and anecdotal reports. It still has a lot to prove in this sense. If it meets expectations, it might be a major product.

Which is superior, Devin AI or ChatGPT? Devin AI is a very good coder, however ChatGPT is a much more comprehensive information base. With its diverse conversational capabilities, it has a usefulness that Devin, with its more focused features, lacks.

Devin AI and ChatGPT are two very different methods to AI coding, each with unique advantages. Devin AI expands the possibilities for self-sufficient software creation, and ChatGPT provides a flexible and user-friendly interface that can be used by a large number of people. Both of these technologies are a part of an ever-accelerating trend that is continuing to change our everyday lives via the use of AI products and services.

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[picloblog]

FleX Exchange - new advisory panel

Photo by Matthew Henry on Unsplash

As announced earlier this year, Piclo secured funding from the Department of Business, Energy & Industrial Strategy (BEIS) to develop an online exchange for multiple flexibility markets. The project is in collaboration with UK Distribution Network Operators, National Grid ESO and Flex Providers registered on the Piclo platform.

The project aims to provide Flex Providers with a common platform to access multiple flexibility markets, lowering barriers to participation. The platform would enable access to “primary markets” such as DSO flexibility competitions and create “secondary markets” for local (DSO) and national (ESO) flexibility services, whereby flex providers could transfer their flexibility contract obligations to another suitable flex provider on a temporary or permanent basis.

In doing so, the Piclo Exchange project aims to show how an open, transparent and neutral flexibility marketplace can play a crucial role in the decarbonisation of Great Britain’s economy. 

To assist with the project, Piclo has assembled a new advisory panel with a mix of experts from across the industry. This advisory panel will provide guidance and critical feedback on the project's progress, discuss opportunities and challenges and ensure the goals and objectives of the project are aligned with the wider policy and regulatory landscape.

The advisory panel’s first meeting was held in October and we are delighted to have input from such an esteemed group of individuals in the energy sector.

Panel Members - who’s who?

Government 

BEIS - Chris Galpin, Senior Policy Advisor

Chris Galpin is a Senior Policy Advisor for the Department for Business, Energy & Industrial Strategy (BEIS). Chris is the policy lead for Distribution System Operation in the Smart Energy team. The UK is developing a world-leading policy and regulatory environment for smart technologies, as set out in the Smart Systems & Flexibility Plan. The development of Distribution System Operation in GB is creating new revenue streams for smart technologies such as energy storage and demand-side response, as well as ensuring efficient operation of the electricity system. Prior to joining BEIS, Chris worked as an engineer. He has experience in low carbon technologies, building services and software development.

Ofgem - Chiara Redaelli, Senior Economist

Chiara leads the work in Ofgem on flexibility markets, focusing on electricity storage, local flexibility and DNO/ESO coordination. For the past 10 years she has been working on a broad range of flexibility issues, having contributed to Ofgem’s first flexibility strategy, as well as participating in European experts groups on flexibility, including storage and demand-side response. Before joining Ofgem, Chiara worked as economic consultant on international projects in the power, water and sanitation sector.

Market representatives   

National Grid ESO - Kayte O’Neill, Head of Markets

Kayte is Head of Markets for the ESO, accountable for developing markets to enable future operation of Great Britain’s electricity system on the path to net zero. She has 20 years of experience in the energy sector in the UK and US, most recently with a focus on designing business models, policy and regulatory frameworks for the rapidly evolving energy landscape.  

Social Energy - Stephen Day, CTO

Steve is CTO of Social Energy, the UKs largest and fasted growing residential energy supply and DSR platform. Steve has 20 years’ experience of technology and product development in the consumer electronics and energy sectors, including at Sharp Electronics. For the last 10 years he has lead highly multidisciplinary R&D teams to develop to renewable and home energy management solutions that deliver flexibility services to the grid. In 2017 Steve co-founded Levelise, a technology start-up that creates AI-led controls for domestic assets to optimise their flexibility. He is a passionate advocate of the contribution domestic energy flexibility can make to the continuing decarbonisation of the grid.

Specialists 

Ignis Markets - Andrew Claxton, Director 

Andrew is a consultant with nearly 25 years’ experience developing liberalised electricity markets.  As CEO, he led the Electricity Pool of England and Wales’s implementation of supply competition to all customers (“1998 Project”).  He was the lead architect in the development of “market coupling” which now integrates the day-ahead wholesale electricity market across nearly all of Europe. 

Energy Networks Association - Randolph Brazier, Head of Innovation 

Randolph is the Head of Innovation & Development at the Energy Networks Association, and has over eight years of experience in the electricity and energy sector in the UK, Europe and Australia. Randolph is responsible for co-ordinating and delivering a broad range of strategic innovation initiatives, including the development of Smart Grids, DSO Transition, Electricity Networks Innovation Strategy, roll-out of Renewables, Battery Storage and other Low Carbon Technologies and the decarbonisation of heat and transport via Heat Pumps and Electric Vehicles. He has previous experience in the design and management of multi-disciplinary power projects in the Transmission & Distribution and renewable generation sectors. Randolph has an M.Phil in Engineering for Sustainable Development from the University of Cambridge and has presented a number of conference papers on electrical systems and protection and automation design.

The Association for Decentralised Energy - Rick Parfett, Policy Manager

Rick is a Policy Manager at the Association for Decentralised Energy (ADE). He focuses primarily on opening up markets and enabling greater penetration of Demand Side Response (DSR) and storage solutions. As we move away from a model based around centralised, fossil fuel-based generation, allowing these innovative business models and disruptive technology to flourish will be key to achieving a flexible, decarbonised system. Before joining the ADE, Rick worked in environmental policy in India and Vietnam, before studying an MSc. at LSE.

Piclo 

James Johnston, CEO and Cofounder

James Johnston is the CEO and Co-founder of Piclo, the UK’s leading online marketplace for local flexibility trading. Prior to Piclo, James spent 3 years researching microgrids at University of Strathclyde. James is also the founder of Solar Sketch, a design company for the solar industry and worked at international engineering firm Arup. James is a published author contributing chapters to Academic Press books on peer-to-peer energy and local flexibility trading.

Alice Tyler, CPO and Cofounder

Alice is the CPO and a Co-founder of Piclo, responsible for product vision, strategy and execution. Her experience as a designer, specifically in user experience design, brings a fresh response to the challenges of achieving net zero in the energy industry.

Kelsey Devine, Innovation Manager

Kelsey is a project manager at Piclo and responsible for the delivery of innovation projects. She has 5 years of experience in the renewable energy industry in Canada and the UK.

Project observers

BEIS - Iliana Cardenes, Energy Systems Flexibility Innovation Lead

Dr Iliana Cardenes leads on energy system flexibility innovation for net zero, in the Science & Innovation for Climate & Energy Directorate within BEIS. She holds a PhD from the University of Oxford’s Environmental Change Institute, with a focus on water and energy systems engineering, a Masters in Climate Science and Policy from Columbia University's Earth Institute in New York City, and an undergraduate degree in Environmental Science from the School of Civil Engineering and the Environment at the University of Southampton. Before joining BEIS, Iliana worked as an energy and climate change specialist at Oxford Policy Management. Previous roles include working on Climate Change Policy for the United Nations Development Programme (UNDP) in Mexico City, on Environmental Statistics at the United Nations Department of Economic and Social Affairs (UN DESA) in New York City, and at the European Commission in Brussels on Environmental Policy.

Mott Macdonald - Douglas Ramsay

Douglas is a chartered electrical engineer with fourteen years’ experience as a consultant for Mott MacDonald. He has a technical focus on distribution networks, storage and renewables. Relevant experience includes: Specialist work on battery energy storage projects considering a number of applications including: frequency support, peak lopping, ramp control on renewable generation, peak shifting on solar generation and islanded operation. Extensive experience working in the planning and design of distribution networks. Developing tools to optimise distribution system network configurations to limit electrical losses and hence carbon emissions.

[fmservers]

Xnor’s saltine-sized, solar-powered AI hardware redefines the edge

“If AI is so easy, why isn’t there any in this room?” asks Ali Farhadi, founder and CEO of Xnor, gesturing around the conference room overlooking Lake Union in Seattle. And it’s true — despite a handful of displays, phones, and other gadgets, the only things really capable of doing any kind of AI-type work are the phones each of us have set on the table. Yet we are always hearing about how AI is so accessible now, so flexible, so ubiquitous.

And in many cases even those devices that can aren’t employing machine learning techniques themselves, but rather sending data off to the cloud where it can be done more efficiently. Because the processes that make up “AI” are often resource-intensive, sucking up CPU time and battery power.

That’s the problem Xnor aimed to solve, or at least mitigate, when it spun off from the Allen Institute for Artificial Intelligence in 2017. Its breakthrough was to make the execution of deep learning models on edge devices so efficient that a $5 Raspberry Pi Zero could perform state of the art computer vision processes nearly well as a supercomputer.

The team achieved that, and Xnor’s hyper-efficient ML models are now integrated into a variety of devices and businesses. As a follow-up, the team set their sights higher — or lower, depending on your perspective.

Answering his own question on the dearth of AI-enabled devices, Farhadi pointed to the battery pack in the demo gadget they made to show off the Pi Zero platform, Farhadi explained: “This thing right here. Power.”

Power was the bottleneck they overcame to get AI onto CPU- and power-limited devices like phones and the Pi Zero. So the team came up with a crazy goal: Why not make an AI platform that doesn’t need a battery at all? Less than a year later, they’d done it.

That thing right there performs a serious computer vision task in real time: It can detect in a fraction of a second whether and where a person, or car, or bird, or whatever, is in its field of view, and relay that information wirelessly. And it does this using the kind of power usually associated with solar-powered calculators.

The device Farhadi and hardware engineering head Saman Naderiparizi showed me is very simple — and necessarily so. A tiny camera with a 320×240 resolution, an FPGA loaded with the object recognition model, a bit of memory to handle the image and camera software, and a small solar cell. A very simple wireless setup lets it send and receive data at a very modest rate.

“This thing has no power. It’s a two dollar computer with an uber-crappy camera, and it can run state of the art object recognition,” enthused Farhadi, clearly more than pleased with what the Xnor team has created.

For reference, this video from the company’s debut shows the kind of work it’s doing inside:

As long as the cell is in any kind of significant light, it will power the image processor and object recognition algorithm. It needs about a hundred millivolts coming in to work, though at lower levels it could just snap images less often.

It can run on that current alone, but of course it’s impractical to not have some kind of energy storage; to that end this demo device has a supercapacitor that stores enough energy to keep it going all night, or just when its light source is obscured.

As a demonstration of its efficiency, let’s say you did decide to equip it with, say, a watch battery. Naderiparizi said it could probably run on that at one frame per second for more than 30 years.

Not a product

Of course the breakthrough isn’t really that there’s now a solar-powered smart camera. That could be useful, sure, but it’s not really what’s worth crowing about here. It’s the fact that a sophisticated deep learning model can run on a computer that costs pennies and uses less power than your phone does when it’s asleep.

“This isn’t a product,” Farhadi said of the tiny hardware platform. “It’s an enabler.”

The energy necessary for performing inference processes such as facial recognition, natural language processing, and so on put hard limits on what can be done with them. A smart light bulb that turns on when you ask it to isn’t really a smart light bulb. It’s a board in a light bulb enclosure that relays your voice to a hub and probably a datacenter somewhere, which analyzes what you say and returns a result, turning the light on.

That’s not only convoluted, but it introduces latency and a whole spectrum of places where the process could break or be attacked. And meanwhile it requires a constant source of power or a battery!

On the other hand, imagine a camera you stick into a house plant’s pot, or stick to a wall, or set on top of the bookcase, or anything. This camera requires no more power than some light shining on it; it can recognize voice commands and analyze imagery without touching the cloud at all; it can’t really be hacked because it barely has an input at all; and its components cost maybe $10.

Only one of these things can be truly ubiquitous. Only the latter can scale to billions of devices without requiring immense investment in infrastructure.

And honestly, the latter sounds like a better bet for a ton of applications where there’s a question of privacy or latency. Would you rather have a baby monitor that streams its images to a cloud server where it’s monitored for movement? Or a baby monitor that absent an internet connection can still tell you if the kid is up and about? If they both work pretty well, the latter seems like the obvious choice. And that’s the case for numerous consumer applications.

Amazingly, the power cost of the platform isn’t anywhere near bottoming out. The FPGA used to do the computing on this demo unit isn’t particularly efficient for the processing power it provides. If they had a custom chip baked, they could get another order of magnitude or two out of it, lowering the work cost for inference to the level of microjoules. The size is more limited by the optics of the camera and the size of the antenna, which must have certain dimensions to transmit and receive radio signals.

And again, this isn’t about selling a million of these particular little widgets. As Xnor has done already with its clients, the platform and software that runs on it can be customized for individual projects or hardware. One even wanted a model to run on MIPS — so now it does.

By drastically lowering the power and space required to run a self-contained inference engine, entirely new product categories can be created. Will they be creepy? Probably. But at least they won’t have to phone home.

Via Devin Coldewey https://techcrunch.com

[millafortner-blog]

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