2026-01-12 02:30:08
Developing and publishing games often involves relying on third-party services, middleware, or platforms. Numerous vendors offer commercial products like advertising networks, backend services, sound engines, user authentication, and more. These tools simplify the process of monetization, expanding player reach, storing data on servers, and performing other functions beyond what a game engine typically provides.
\ To facilitate integration, these vendors often supply SDKs tailored for specific game engines, allowing developers to easily install them and interact with their products.
\ Godot, while rapidly growing in popularity, is still relatively a newcomer. As such, while some vendors are beginning to support the engine, many others have not yet invested on it, given the necessary development and maintenance costs.
\ For some, this is due to the significant resources required (finding the right developers to hire to make the SDKs), while for others, it’s simply a matter of being unaware of Godot’s growing market or dealing with internal bureaucracy that slows down the process of supporting the engine.
\ As the Godot Foundation is committed to using donations solely for creating FOSS and prioritizing open standards, it does not allocate resources from the general pool of donations to integrate proprietary SDKs into the engine.
\ While officially incorporating monetization SDKs into Godot might not align with the project’s goals, for example, there is still a widely recognized need within the community for such support to help developers make a living. As there is a need for many other SDKs.
To tackle this challenge, many in the Godot community have taken matters into their own hands, developing proper integrations for third-party services. Talented developers have done an incredible job to bring the most popular services into Godot, enabling the community to publish games on their desired platforms using the necessary third-party tools.
\ However, this approach still presents several issues for the community:
\ Adding to this, some vendors who provide SDKs for other game engines have expressed interested in supporting Godot but are not ready to allocate internal resources to develop an integration due to costs or other constraints. They might be willing to donate their integration (or donate the money to do an integration) to the Godot Foundation and collaborate with the community.
\ While this is amazing, the Foundation doesn’t want to become responsible for maintaining such integrations using general funds once the specific donation runs out.
After extensive discussions with the community and various vendors, the Godot Foundation has decided to maintain its stance on not directly developing SDKs. However, recognizing the need for a more organized approach, the Foundation has established an official centralized GitHub organization for hosting SDK integrations, thereby encouraging community contributions.
\ This organization eases the burden on developers who create their own SDK bindings, as they will now be able to accept contributions without bearing the sole responsibility for maintaining the integration. It also provides a clear and official platform for companies that wish to donate their SDK integrations, offering a centralized location for these contributions.
\
The new Godot SDK Integrations organization has its own GitHub page already. Developers who have been working on SDK integrations are invited to move their work here, if they desire so. You can join the new sdk-integrations channel to discuss this transfer.
\ Likewise, vendors interested in donating integrations for their first-party SDKs are also welcome to move them to this new organization, if they deem it preferable to hosting SDKs themselves. Please contact the Godot Foundation in order to make arrangements. Likewise, if you would like to finance the development of Godot integrations for your SDKs, the Godot Foundation can help facilitate contacts with trusted community maintainers or companies who can take on such work.
\ Please note that this organization is a public space for centralizing SDK integration efforts and collecting repositories. The Foundation will not manage or update the integrations, so ongoing development will depend on the community and vendor contributions. This initiative is intended to be a collective effort.
As we mentioned, the Godot Foundation is hosting this new space, but doesn’t intend to spend its resources on maintaining it.
\ We are therefore calling for motivated community members to get in touch and help organize the structure of this new organization, and help support developers and vendors who want to provide new SDK integrations for Godot.
\ Aside from the actual work of developing and maintaining SDK integrations, we foresee the need for contributors with a production profile who can help write common contribution guidelines, instructions on how to “recruit” new maintainers, or generally ensure some quality and fitness for purpose of the various SDKs offered by and to the community.
\
For the time being, we are going to discuss and organize the work of this community organization on the #sdk-integrations channel of the Godot Contributors Chat. See you there!
Godot Foundation
\ Also published here
\ Photo by Vardan Papikyan on Unsplash
\
2026-01-12 02:00:21
Convex Relaxation Techniques for Hyperbolic SVMs
Discussions, Acknowledgements, and References
\
B. Solution Extraction in Relaxed Formulation
C. On Moment Sum-of-Squares Relaxation Hierarchy
E. Detailed Experimental Results
F. Robust Hyperbolic Support Vector Machine
Support Vector Machine (SVM) is a classical statistical learning algorithm operating on Euclidean features [10]. This convex quadratic optimization problem aims to find a linear separator that classifies samples of different labels and has the largest margin to data samples. The problem can be efficiently solved through coordinate descent or Lagrangian dual with sequential minimal optimization (SMO) [11] in the kernelized regime. Mature open source implementations exist such as LIBLINEAR [12] for the former and LIBSVM [13] for the latter.
\ Less is known when moving to statistical learning on non-Euclidean spaces, such as hyperbolic spaces. The popular practice is to directly apply neural networks in both obtaining the hyperbolic embeddings and perform inferences, such as classification, on these embeddings [2, 3, 14–20]. Recently, rising attention has been paid on transferring standard Euclidean statistical learning techniques, such as SVMs, to hyperbolic embeddings for both benchmarking neural net performances and developing better understanding of inherent data structures [4–7]. Learning a large-margin solution on hyperbolic space, however, involves a non-convex constrained optimization problem. Cho et al. [4] propose and solve the hyperbolic support vector machine problem using projected gradient descent; Weber et al. [7] add adversarial training to gradient descent for better generalizability; Chien et al. [5] propose applying Euclidean SVM to features projected to the tangent space of a heuristically-searched point to bypass PGD; Mishne et al. [6] reparametrize parameters and features back to Euclidean space to make the problem nonconvex and perform normal gradient descent. All these attempts are, however, gradient-descent-based algorithms, which are sensitive to initialization, hyperparameters, and class imbalances, and can provably converge to a local minimum without a global optimality guarantee.
\ Another relevant line of research focuses on providing efficient convex relaxations for various optimization problems, such as using semidefinite relaxation [8] for QCQP and moment-sum-ofsquares [21] for polynomial optimization problems. The flagship applications of SDP includes efficiently solving the max-cut problem on graphs [22] and more recently in machine learning tasks such as rotation synchronization in computer vision [23], robotics [24], and medical imaging [25]. Some results on the tightness of SDP have been analyzed on a per-problem basis [26–28]. On the other hand, moment-sum-of-squares relaxation, originated from algebraic geometry [21, 29], has been studied extensively from a theoretical perspective and has been applied for certifying positivity of functions in a bounded domain [30]. Synthesizing the work done in the control and algebraic geometry literature and geometric machine learning works is under-explored.
\
:::info Authors:
(1) Sheng Yang, John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA ([email protected]);
(2) Peihan Liu, John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA ([email protected]);
(3) Cengiz Pehlevan, John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, Center for Brain Science, Harvard University, Cambridge, MA, and Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University, Cambridge, MA ([email protected]).
:::
:::info This paper is available on arxiv under CC by-SA 4.0 Deed (Attribution-Sharealike 4.0 International) license.
:::
\
2026-01-12 02:00:17
From the beginning, the focus of this blog has been technical, very rarely organizational. I broke this unwritten rule once in 2015. I began writing retrospectives in 2023, on the year that had passed. Let's continue the tradition, but with a wider scope than before. The situation warrants it.
It's a hard realization to admit, but the world is spiraling deeper and deeper into chaos. Russia's invasion of Ukraine has now dragged into its third year, and still, neither the European Union nor NATO has mustered the resolve to intervene militarily. Every day brings fresh reports of Russian war crimes, met only with our silent complicity. The strongest response we seem capable of is to "strongly condemn."
\ What stings most is the betrayal by the United States, an ally whose reliability is increasingly coming into question. As a signatory to the Budapest Memorandum, America was bound to uphold Ukraine's sovereignty. Under President Biden, support has been cautious at best. Now, they are pressuring Ukraine to cede its own land, a demand that flies in the face of Ukraine's constitution. The reasons for this abandonment remain unclear, but the consequences are far-reaching.
\ History offers a grim parallel: the Roman Empire once maintained order across the Mediterranean and beyond. While we can't know the daily lives of its citizens, those within its borders enjoyed relative peace: the Pax Romana. When Rome fell, so too did stability, plunging the world into ever-increasing chaos and bloodshed. The parallel with the current "Pax Americana" is too evident to deny; only the consequences are far more global. Check the Uppsala Conflict Data Program for the backing data.
\ Worse, just yesterday, the USA kidnapped the President of Venezuela, without Congress's approval. It opens the way to more and more rash actions from state or para-state actors: China attacking Taiwan might very well be next, with severe consequences all over the globe.
The DevRel state is intrinsically bound to the world state.
\ I started working as a Developer Advocate in 2018 after 17 years of engineering. I wanted to spend more time producing content and talking at conferences, which I did. I had lots of fun, but I got fired in 2024. It took me a couple of months to find another job, but I got fired again in 2025.
\ DevRel is an important discipline, but it comes with a couple of issues:
\ When the economy is shrinking, the closer your job is to the end of the funnel, the safer your position is. DevRel sits at the beginning of the sales funnel. For this reason, I decided to return to engineering.
Regardless of the bleak context, I still have a couple of personal wins this year.
When I made a sideways career step to work in DevRel, Developer Advocate was a growing market. It was easy to move there as an engineer, even if you weren't technical at all. I thought returning to engineering would be challenging given the current state of the market. I actually found a new position in a few months. While the decision to leave DevRel was hard, I consider myself lucky. I'm now happily working for a banking solutions editor.
\ Along with friends and family, a good job is a stable anchor in your life.
I went to Australia, and I came back: it was amazing! My last blog post of 2025 was all about it.
My good friend Richard Fichtner recommended that I become an Oracle Ace, and it became true.
Thanks, Richard!
On a personal level, I feel confident enough: I have built some marketable skills over the years. On a global level, though, I'm very worried about the next decade. My advice to you is: never stop learning and be very agile. Take care.
Originally published at A Java Geek on January 4th, 2025
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2026-01-12 02:00:12
In 2025–2026, a new approach to building software is quietly gaining traction — one that challenges traditional notions of writing code line by line and recasts developers as orchestrators of intelligent systems rather than manual typists of syntax.
\ The term “vibe coding”, popularized in late 2025 — refers to a development style in which AI models generate production-ready code based purely on natural-language instructions and iterative feedback, rather than human engineers meticulously writing every line by hand.
\ This isn’t just a gimmick. It reflects a fundamental shift in how developers interact with machines, and it has major implications for SaaS platforms, software engineering teams, developer tools, and future tech workflows.
At its core, vibe coding flips the traditional development process on its head.
\ Instead of:
human writes exact code → tests → deploys,
\ developers now do:
developer describes intent in natural language → AI generates code → developer verifies & iterates
\ This means:
\ What sets vibe coding apart isn’t merely that AI generates code — it’s that the human doesn’t read every line of the output. Instead, developers judge success by outcomes: does it run? Does it pass tests? Does it meet requirements?
\ In that sense, the developer’s role becomes more conceptual, strategic, and experimental.
Modern SaaS products increasingly need rapid iteration. Vibe coding allows teams to prototype features in hours — not weeks — by focusing humans on critical thinking rather than typing routine syntax.
Because AI bridges the gap between idea and implementation, domain experts without classical software training can increasingly build and validate tools. This democratizes software creation — but also raises questions about quality, ownership, and governance.
Contrary to doom-scroll headlines, AI code generation doesn’t make developers obsolete. It changes what developers do.
\ Instead of focusing on syntax and implementation details, developers can concentrate on:
\ This could redefine engineering roles toward architects of systems and validators of logic.
Vibe coding isn’t without risks:
AI models sometimes hallucinate — generating code that looks plausible but contains bugs or security flaws. This makes rigorous testing and validation workflows essential.
Generated code may unknowingly introduce vulnerabilities or use unsafe dependencies. Teams need tooling that analyzes AI-generated outputs for security and compliance.
If developers no longer write most of the code, how does a team ensure maintainability? How do you debug code you've never seen? These are real questions for engineering leaders adopting vibe coding.
SaaS platforms that integrate AI-assisted development workflows could unlock:
Expect a new generation of tools oriented around:
Team structures may evolve:
\ This creates opportunities — but also requires new skill sets around AI governance, prompt design, and system orchestration.
Vibe coding suggests that the future of engineering isn’t less human — it’s different human.
\ Developers won’t disappear; they’ll evolve. The key skills will shift from writing code to designing thoughtful instructions, validating AI outputs rigorously, and orchestrating complex systems.
\ This trend isn’t just hype. It’s already influencing how startups prototype features, how SaaS companies innovate, and how developer workflows scale with intelligence at every layer. For engineers, product managers, and tech founders alike, understanding and adapting to this shift isn’t optional — it’s essential.
2026-01-12 02:00:06
The history of artificial intelligence (AI) dates back to the 1950s and has evolved significantly. Today, AI plays a prominent role for both individuals and organizations. In 2024, corporate investment in AI reached $252.3 billion, a 13-fold increase from 2014. Demand is high, and everyone is hungry for revenue growth and cost reduction.
\ Most recently, Generative AI has created a tidal wave across industries. Based on a survey by Google Cloud, 74% of respondents reported ROI on GenAI investments. Yet, the evolution didn’t stop there, with the next leap being the rise of AI Agents. Still in its infancy, the capabilities of AI Agents have yet to be fully explored. So, is this technology as promising as it seems?
Generative AI operates by receiving data and following instructions to produce outputs. On the other hand, AI Agents are designed to learn and act autonomously to achieve predefined goals. For instance, a banker might provide a GenAI with data and ask it to generate a report.
\ The AI produces the report accordingly, but can’t act independently and still requires a human to review it. AI Agents go a step further; rather than just following rules, they can interpret each case and make suggestions or even make decisions without human intervention within a defined regulatory and risk-control framework. Accordingly, it is evident that technology has advanced from passive content generation to autonomous agentic solutions, creating new opportunities for investment returns.
Here are some tasks AI Agents can handle when properly trained:
Some companies are leading in this technological movement through early adoption. For example, BNY has its own enterprise AI platform named Eliza, which offers multiple AI models from leading providers for BNY employees. “Digital workers” at BNY find new business leads, write code, handle payment processes and client onboarding, and handle reconciliations. Currently, BNY reports having over 100 digital employees.
\ Moreover, JPMorgan Chase’s agentic deployment demonstrates its capabilities empirically. They introduced LAW, which consists of multiple specialized agents in the legal domain that respond to complex legal queries. The study’s empirical benchmark consists of a dataset of 720 queries. Accordingly, LAW excelled in complex tasks compared to the baseline, which is GPT-3.5-turbo (GenAI). For instance, in calculating contract termination dates, LAW performed 92.9% better than the baseline.
Indeed, we anticipate investment growth for companies that successfully implement AI Agents. However, there are several challenges that investors need to consider when assessing companies’ AI approaches:
The proper deployment of AI Agents
In general, across different AI initiatives, extracting value from these models remains a challenging process. An IBM survey of 2,000 CEOs found that only 25% of AI initiatives delivered the expected ROI over the past three years, and just 16% scaled at the enterprise level. So, for companies, having a budget isn’t enough.
\ Moreover, history shows that technological potential alone isn’t enough and that it requires proper deployment. According to an MIT study, this was evident with GenAI implementations, which were often fragmented or poorly launched. Based on executive interviews, this study found that 95% of organizations with GenAI models are getting zero return, while a small portion is “extracting millions in value.” Although this study has its limitations, as it only measured ROI six months post-pilot, it highlights the issue: misapplication rather than technological failure.
“Agentic washing”
This occurs when companies or vendors claim their AI systems have agentic qualities, but in reality, they do not. After analyzing thousands of “supposedly” Agentic AI vendors, Gartner analysts report that only around 130 products exhibit agentic traits. Accordingly, Gartner projects that over 40% of agentic projects will be canceled by the end of 2027 due to factors such as “unclear business value, inadequate risk controls, or escalating costs.” Therefore, as investors, it is important to look beyond marketing claims.
First movers gaining an edge
IBM research highlights a clear performance gap between AI-first organizations and those with gradual implementations (see Figure 1).
So, AI-first organizations demonstrate improvements in revenue and operating profits compared to their other AI initiatives. They are more likely to realize measurable ROI. Google Cloud reports similar findings, reinforcing the link between early strategic commitment and realized ROI.
The hype surrounding Agentic AI is real. Based on surveys and interviews with over 2,000 respondents, an MIT Sloan report states that 35% of companies are already using Agentic AI, and another 44% plan to adopt it soon. For us, investors, enthusiasm alone doesn’t create shareholder value. Proper evaluation builds confidence when investing in a company:
\ When all the criteria above are met, Agentic AI has genuine potential to deliver realized ROI and improve stock performance.
2026-01-12 01:59:59
Sam Altman says a single founder will soon build a billion-dollar company. Altman asks: How soon until a single person boots a state?
