http://softwareplanetgroup.co.uk/

Software Planet Group, Austen House, Station View, Guildford (2026)

29/08/2026

12 November 2026 could be the day Cursor loses its built-in GPT models.

OpenAI is terminating its agreement with Cursor following the company's acquisition by SpaceX. And this is probably one of the most interesting break-ups we have seen in the AI industry so far. Formally, everything is very civilised. OpenAI has proposed the maximum transition period allowed under the agreement, apparently to give developers time to adapt. But the explanation behind the decision is considerably less polite.

OpenAI says it cannot be confident that its technology, when used by Elon Musk-controlled companies, will continue to be used in accordance with its terms. And then comes the history. After Musk acquired Twitter, OpenAI says contractual terms were violated. OpenAI also points to Musk's testimony that xAI used OpenAI outputs in the development of its own models.

In other words, the neighbour has already been caught looking inside your fridge, and is now surprised that you have changed the lock. There is another particularly interesting detail. In the same announcement, OpenAI mentions the name of an upcoming model: Astra. It also makes it clear that future models will not be made available to Cursor.

And the proposed Cursor cut-off date is 12 November. Pure speculation on our part, but the coincidence is hard to ignore: could 12 November also be connected with Astra's launch?

If so, the message would be rather brutal. Cursor keeps the existing models during the transition period, but gets thrown overboard just as the next generation arrives.

There is obviously a personal history behind all of this as well. Sam Altman and Elon Musk once founded OpenAI together. Today, their disagreement is being played out through lawsuits, contracts, competing AI companies and now access to models. But the people who ultimately feel the consequences are developers.

If a large part of your engineering workflow depends on Cursor and GPT models, a disagreement between two billionaires can suddenly become your infrastructure problem.

Fortunately, this is not an emergency. Cursor users can still use their own OpenAI API keys, and there are other models available inside the editor. But there is a broader engineering lesson here. AI models are becoming infrastructure. And just like databases, cloud providers or external APIs, they create dependencies.

At Software Planet Group, we think AI-assisted development processes should be designed with this in mind: models should be replaceable components, not foundations that the entire engineering workflow depends on.

Because apparently, even your IDE can now become collateral damage in someone else's divorce.

19/08/2026

We certainly never expected the day when the Software Planet Group blog would be giving clients advice on how to build software themselves with generative AI. Yet here we are.

The competitive landscape has changed. We are no longer competing only with other bespoke software development companies. In some cases, we are also competing with our own clients, who can now turn an idea into working code or an MVP prototype before they ever speak to an engineering team. And we actually see that as a positive development.

Increasingly, clients come to us with much more than an idea. They bring prototypes, AI-generated codebases and early versions of real products. That means our role can move further towards the work where experienced engineering judgement matters most: understanding what has been built, identifying architectural and quality risks, preserving what is valuable, and turning an experimental product into software that can be maintained, scaled and developed with confidence.

Many development companies would rather start again from a clean repository. We have always been comfortable doing something harder: working with existing code, stabilising it, refactoring it and gradually turning it into a strong product without unnecessarily throwing away what already works.

Perhaps generative AI does not remove the need for software engineering. It simply moves the starting line.

The article turned out to be rather more extensive than we initially expected, so we’ll be publishing it in several parts. Keep an eye out for the next instalment.

https://softwareplanetgroup.co.uk/planning-software-when-ai-writes-it/

08/07/2026

The pace of change in agentic AI is unlike anything we've seen since the early days of Agile. Six months ago, "harness" was a niche term; today it's how the most effective engineering teams run AI agents in production — and at a recent hackathon, 19 of the top 20 teams had built their solutions on one. If you blinked, you may well have missed the shift.

And that's the problem. For many engineering leaders, the landscape has moved so quickly — from prompt engineering to context engineering to harness building in barely two years — that it's genuinely hard to tell where the industry is heading, and which of today's approaches will still matter next quarter.

Our latest article is an attempt to bring some clarity. We trace how agents used in software development have conceptually evolved: from simple chat models, through the elaborate scaffolding era of LangChain and LangGraph, to the surprising simplification that produced modern harnesses like Claude Code and Hermes. We explain why skills — not bigger models or longer context windows — are the evolutionary point the industry has just reached, what makes them fundamentally different from the MCP-style tooling of a year ago, and where the road leads from here (hint: it involves agents that maintain and improve their own capabilities).

If you're deciding how AI agents should fit into your delivery process — or simply want to understand what your engineers mean when they say "harness" — this one's for you.

https://softwareplanetgroup.co.uk/ai-agent-skills-new-memory/

30/06/2026

For years, we viewed AI coding tools with healthy scepticism. They were impressive demonstrations, useful in places, but nowhere near capable of changing how professional software engineering teams actually worked. We kept asking ourselves the same question: Is this real yet?

At some point over the past year, without any announcement or grand revelation, our answer changed. AI stopped being just another productivity tool and became capable of delivering substantial end-to-end engineering work. We believe we have quietly crossed a threshold where autonomous engineering tasks are becoming self-contained building blocks from which increasingly sophisticated software systems can be assembled. The implications extend far beyond software development itself.

In our latest article, we explain why we believe this is not simply another technological improvement, but the beginning of a fundamental shift in what software engineering is becoming.

https://softwareplanetgroup.co.uk/the-end-of-yesterdays-software-development/

23/06/2026

Reducing SaaS Churn Through Better Product Design

At Software Planet Group, we specialise in designing and developing SaaS products for startups, scale-ups, and established businesses. Over the years, we have seen that reducing churn is rarely about adding more marketing campaigns or aggressive retention tactics. More often, it comes down to thoughtful product design that aligns with the way customers actually use software.

In our latest article, we explore practical approaches that help SaaS companies retain customers without creating frustration or damaging user trust. From subscription pause options and flexible billing models to better lifecycle design, these strategies can significantly improve long-term retention while strengthening customer relationships.

If you are building or scaling a SaaS platform, these are product decisions worth considering.

Read the full article:

https://softwareplanetgroup.co.uk/reducing-saas-churn-through-better-product-design/

19/06/2026

Most enterprise AI discussions today focus on larger context windows. Models can now process hundreds of thousands, or even millions, of tokens. But does having access to more information automatically lead to better reasoning?

In our latest article, we explore why larger context windows alone may not solve complex reasoning challenges, how Retrieval-Augmented Generation (RAG) differs from the emerging Recursive Language Model (RLM) approach, and why the future of enterprise AI is likely to combine retrieval, tool usage, code ex*****on, and recursive reasoning into hybrid architectures.

We also examine recent MIT research that suggests system architecture may have a greater impact on AI performance than simply increasing model size or context length.

If you're building AI-powered products, enterprise knowledge systems, developer tools, or decision-support platforms, this is a discussion worth following.

Read the full article here:
https://softwareplanetgroup.co.uk/rlm-changing-enterprise-ai/

13/06/2026

By now, you have probably heard that the US Government has instructed Anthropic to suspend access to its latest frontier models.

We have been arguing for some time that a move of this kind was not only possible, but increasingly likely. In our view, the real question was never whether governments would intervene, but when.

Some time ago, we set out our thoughts on the logic behind this process, the challenges of regulating increasingly capable AI systems, and whether it is still possible to slow, redirect, or meaningfully control the trajectory of AI development.

You can read our perspective here:

https://softwareplanetgroup.co.uk/ai-has-escaped-the-lab/

The article ends with a question that now feels more relevant than ever: if AI has already escaped the lab, where exactly are we on that journey today?

06/05/2026

It feels like the AI race is entering a very different phase.

One of the more unexpected developments recently: Musk is reportedly allocating capacity from his 1GW Colossus data centre to Anthropic. And that looks rather significant.

Not long ago, Colossus was being positioned as infrastructure built primarily for Grok and xAI. At the same time, Musk had repeatedly criticised Anthropic publicly, often dismissing the company and its approach altogether.

But the market is moving fast.

Anthropic is seeing explosive demand for Claude, and the company clearly needs additional compute capacity to keep up. Ironically, part of that demand may now be supported by infrastructure originally intended to strengthen a direct competitor.

At the same time, Grok appears to be losing momentum.

A good example is Vending Bench - a benchmark focused on long-horizon agentic capabilities. Around the launch of Grok 4, the model scored roughly 4.6k, while Claude 4 was closer to 2k.

Today, Opus 4.7 reportedly exceeds 10k, while Grok’s progress appears far more limited.

And this may be the most important point.

AI development is starting to compound on itself. Leading labs openly discuss how their own models already assist with coding, research, evaluation pipelines and even parts of model development itself.

In other words, stronger models increasingly help build even stronger models.

Which means that once a company starts falling behind, catching up becomes exponentially harder because the leaders are accelerating at the same time.

Against that backdrop, the departure of senior engineers from xAI and the relatively quiet Grok 4.2 release without strong benchmark positioning begin to look more meaningful.

Does this mean xAI is out of the race? Probably too early to say that. Musk has repeatedly shown an ability to make unexpected moves and recover difficult situations.

But allowing a direct competitor access to your flagship infrastructure does look like a strong signal of just how intense the competition in AI has become.

And right now, Anthropic increasingly looks like one of the biggest beneficiaries of this phase of the market.

Curious to hear other views - can xAI realistically close the gap over the next 12-18 months?

27/04/2026

AI is once again reshaping how we think about product design and development.

Two new tools from major players are starting to influence not only how interfaces are created, but also how teams approach UX decisions, prototyping, and even collaboration between designers and engineers. At first glance, both solutions look impressive - but the real story sits beneath the surface.

There are subtle, yet critical differences in how they operate, what problems they actually solve, and where their limitations begin. Some of the bold claims don’t fully hold up in practice, while other capabilities are more impactful than they initially appear.

We took a closer look to separate genuine innovation from well-packaged marketing, and to understand what this means for developers, product teams, and businesses making decisions today.

Full breakdown in the article.

https://softwareplanetgroup.co.uk/claude-design-vs-google-stitch/

12/04/2026

A client approached us recently with a product entering a market that already has established alternatives. The obvious question was not just how to build it, but how to make it meaningfully different. Our suggestion was straightforward: invest in the user interface as a way to address real user pain points and create that differentiation.

Unfortunately, that path was not taken. It is a familiar pattern, and it usually leads to the same place. This experience prompted us to put together our perspective on the role of UI design in modern web services, and why it is often underestimated until it becomes a problem worth fixing.

https://softwareplanetgroup.co.uk/the-hidden-cost-of-ui/

Software Planet Group

29/08/2026

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08/07/2026

30/06/2026

23/06/2026

19/06/2026

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