#devin ai software engineer

[mysticrubs]

Devin AI software engineer

Meet Devin AI Software Engineer: Its Uses and Capabilities

[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.

[usaii]

Devin AI: The Newest AI Software Engineer on the Block | USAII®

A perfect companion for software engineers is here. Devin AI rules the AI coding world while complementing efficiently skilled AI engineers with top AI tools and GenAI.

Read more: https://shorturl.at/klhZ1

DEVIN AI, AI engineering, AI Software Engineer, AI programmers, conversational AI, AI tools, Software Developer, AI engineer, Generative AI

[latestglobalnewswala]

According to many AI and Tech experts worldwide, Devin is the only AI solution in this category that does all the development tasks by itself. It is capable of writing software architecture, coding the application, and in the end delivering the whole solution without human intervention. Website: https://globalnewswala.com/

[rightnewshindi]

दुनिया का पहला AI सॉफ्टवेयर इंजीनियर 'डेविन' हुआ लॉन्च, जानें क्या है खासियतें

दुनिया का पहला AI सॉफ्टवेयर इंजीनियर 'डेविन' हुआ लॉन्च, जानें क्या है खासियतें

AI Software Engineer: दुनिया के पहले AI सॉफ्टवेयर इंजीनियर को लॉन्च किया गया है। यह एआई टूल इतना स्मार्ट है कि कोड लिख सकता है। यह वेबसाइट और सॉफ्टवेयर बना सकता है। इसे टेक कंपनी क��ग्निशन ने बनाया है। इसे डेविन नाम दिया गया है। डेविन को आप जो कहेंगे करेगा। कॉग्निशन ने बताया है कि डेविन को इस इरादे से नहीं बनाया गया है कि आगे ��लकर यह मानव इंजीनियरों की जगह ले। इसे इंसानों के साथ हाथ से हाथ मिलाकर…

[hackernewsrobot]

Devin, the First AI Software Engineer

https://www.cognition-labs.com/blog

[athomewiththecicadas]

6/3/25

I'm gonna start calling him Thom Thumb. Just like when he was a kid. Thom Thumb using AI to find his way home. With the lawn watering timer.

Thom Thumb in the botanical bunker. And after my spouse serves her burritos and chimichanga, while we play house all day, we can experience once again, our lovely and reliable...even dependable cups of Robusta Coffees.

In ceramic cups and mugs.

Sitting him once more in his Devine placement for lost souls. As I shut her mouth... The spirits of his feminine side.

You know in your inner soul she is not lost with me.

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6/4/25

TSMC is reporting they are doing 2nm lithography with a 90% yield on ASML DUV Lithography Machines.

This is not only impressive, it is institutionally remarkable. At least in my mind.

This is stating that the raw technique and skillset of institutional craftsmanship, really stands up the quality of ASML Machinery, but the Chemistry of Lithography as well.

Original content is one thing to speak of, but this business is promoting the quality of hand craftsmanship, far beyond what these companies are advertising.

Maintaining high quality at very low equipment enumerations is a very difficult trade to offer in the modern industry. But producing this kind of product quality at minimal equipment enumerations is something that really leaves merit to the idea of leaving the traditional system of commerce, for something as a payroll career.

I've spent plenty of time with the chemistry of lithography. Especially when you are talking about chip quality that goes in consumer products. But these practices are also suggesting that a true valuation can't exactly be reported in a standard financial forum. Because the quality of human capital, really has produced a product that exceeds expectations. Not to mention the quality of the equipment, materials, and procedure that goes with it.

What makes technology amazing is when it is done right, within the enumeration of how it's applied.

This is merely stating that the quality of career opportunity, by the procedure of a business model, is generating a success in an industry that has never seen this kind of quality before.

What this translates to is providing the confidence in the industry for large Software Corporations, such as Microsoft and Google, to really get back to the institutional values of computation itself. And providing enough incentive, with the reliability of such a manufacturing process, for these Software Companies to develop their products into a tangible Hardware Asset.

As far as market share, a hardware asset is a billable real estate. Making Software Companies more open to finalizing institutional values for their own business, but turning the tech sector into the real estate values the institution condones and encourages.

Besides the idea of a traditional computer processor, and how it is engineered, not only does this expand the demand for hardware engineering, it allows tech companies to distinguish themselves in market share and corporate entity, by the hardware assets they would be developing.

That's a completely different ideology of what we think of when you talk about the "virtual world" of technology in whole.

[infernovm]

Putting agentic AI to work in Firebase Studio

Agents are the big story in AI now. Putting agentic AI to work in software engineering can be done in a variety of ways. Some agents work independently of the developer’s environment, working essentially like a remote developer. For example, Cognition AI’s Devin has its own workspace, complete with a shell, code editor, and web browser. Other agents work directly within the developer’s own…

[sunaleisocial]

A faster way to solve complex planning problems

New Post has been published on https://sunalei.org/news/a-faster-way-to-solve-complex-planning-problems/

A faster way to solve complex planning problems

When some commuter trains arrive at the end of the line, they must travel to a switching platform to be turned around so they can depart the station later, often from a different platform than the one at which they arrived.

Engineers use software programs called algorithmic solvers to plan these movements, but at a station with thousands of weekly arrivals and departures, the problem becomes too complex for a traditional solver to unravel all at once.

Using machine learning, MIT researchers have developed an improved planning system that reduces the solve time by up to 50 percent and produces a solution that better meets a user’s objective, such as on-time train departures. The new method could also be used for efficiently solving other complex logistical problems, such as scheduling hospital staff, assigning airline crews, or allotting tasks to factory machines.

Engineers often break these kinds of problems down into a sequence of overlapping subproblems that can each be solved in a feasible amount of time. But the overlaps cause many decisions to be needlessly recomputed, so it takes the solver much longer to reach an optimal solution.

The new, artificial intelligence-enhanced approach learns which parts of each subproblem should remain unchanged, freezing those variables to avoid redundant computations. Then a traditional algorithmic solver tackles the remaining variables.

“Often, a dedicated team could spend months or even years designing an algorithm to solve just one of these combinatorial problems. Modern deep learning gives us an opportunity to use new advances to help streamline the design of these algorithms. We can take what we know works well, and use AI to accelerate it,” says Cathy Wu, the Thomas D. and Virginia W. Cabot Career Development Associate Professor in Civil and Environmental Engineering (CEE) and the Institute for Data, Systems, and Society (IDSS) at MIT, and a member of the Laboratory for Information and Decision Systems (LIDS).

She is joined on the paper by lead author Sirui Li, an IDSS graduate student; Wenbin Ouyang, a CEE graduate student; and Yining Ma, a LIDS postdoc. The research will be presented at the International Conference on Learning Representations.

Eliminating redundance

One motivation for this research is a practical problem identified by a master’s student Devin Camille Wilkins in Wu’s entry-level transportation course. The student wanted to apply reinforcement learning to a real train-dispatch problem at Boston’s North Station. The transit organization needs to assign many trains to a limited number of platforms where they can be turned around well in advance of their arrival at the station.

This turns out to be a very complex combinatorial scheduling problem — the exact type of problem Wu’s lab has spent the past few years working on.

When faced with a long-term problem that involves assigning a limited set of resources, like factory tasks, to a group of machines, planners often frame the problem as Flexible Job Shop Scheduling.

In Flexible Job Shop Scheduling, each task needs a different amount of time to complete, but tasks can be assigned to any machine. At the same time, each task is composed of operations that must be performed in the correct order.

Such problems quickly become too large and unwieldy for traditional solvers, so users can employ rolling horizon optimization (RHO) to break the problem into manageable chunks that can be solved faster.

With RHO, a user assigns an initial few tasks to machines in a fixed planning horizon, perhaps a four-hour time window. Then, they execute the first task in that sequence and shift the four-hour planning horizon forward to add the next task, repeating the process until the entire problem is solved and the final schedule of task-machine assignments is created.

A planning horizon should be longer than any one task’s duration, since the solution will be better if the algorithm also considers tasks that will be coming up.

But when the planning horizon advances, this creates some overlap with operations in the previous planning horizon. The algorithm already came up with preliminary solutions to these overlapping operations.

“Maybe these preliminary solutions are good and don’t need to be computed again, but maybe they aren’t good. This is where machine learning comes in,” Wu explains.

For their technique, which they call learning-guided rolling horizon optimization (L-RHO), the researchers teach a machine-learning model to predict which operations, or variables, should be recomputed when the planning horizon rolls forward.

L-RHO requires data to train the model, so the researchers solve a set of subproblems using a classical algorithmic solver. They took the best solutions — the ones with the most operations that don’t need to be recomputed — and used these as training data.

Once trained, the machine-learning model receives a new subproblem it hasn’t seen before and predicts which operations should not be recomputed. The remaining operations are fed back into the algorithmic solver, which executes the task, recomputes these operations, and moves the planning horizon forward. Then the loop starts all over again.

“If, in hindsight, we didn’t need to reoptimize them, then we can remove those variables from the problem. Because these problems grow exponentially in size, it can be quite advantageous if we can drop some of those variables,” she adds.

An adaptable, scalable approach

To test their approach, the researchers compared L-RHO to several base algorithmic solvers, specialized solvers, and approaches that only use machine learning. It outperformed them all, reducing solve time by 54 percent and improving solution quality by up to 21 percent.

In addition, their method continued to outperform all baselines when they tested it on more complex variants of the problem, such as when factory machines break down or when there is extra train congestion. It even outperformed additional baselines the researchers created to challenge their solver.

“Our approach can be applied without modification to all these different variants, which is really what we set out to do with this line of research,” she says.

L-RHO can also adapt if the objectives change, automatically generating a new algorithm to solve the problem — all it needs is a new training dataset.

In the future, the researchers want to better understand the logic behind their model’s decision to freeze some variables, but not others. They also want to integrate their approach into other types of complex optimization problems like inventory management or vehicle routing.

This work was supported, in part, by the National Science Foundation, MIT’s Research Support Committee, an Amazon Robotics PhD Fellowship, and MathWorks.

[qhsetools2022]

OpenHands: Open Source AI Software Developer

Image by Author   Have you heard about Devin, which claims it can replace software engineers with an AI system for just $500 a month? There has been a lot of hype surrounding the idea that AI will soon replace software engineers, enabling them to build, test, and deploy applications in minutes with minimal supervision. There’s also a tool called “OpenHands,” which is essentially an open-source…

[ericvanderburg]

The world's 'first AI software engineer' isn't living up to expectations: Cognition AI's 'Devin' assistant was touted as a game changer for developers, but so far it's fumbling tasks and struggling to compete with human workers

http://securitytc.com/THmlwd

[3acesnews]

AI Breakthrough: Devin, the Self-Programming Software Engineer, Raises Eyebrows in Tech