Category: Daily Report

  • Day 58 – 60- Quick Catchup & MCP -Model Context Protocol Revolution

    It’s been a few days since I posted last. I’m going to let myself off, but resolve to do better in the future – since I do want to keep posting on a daily basis but I am aware I want to make the posts more valuable; so I feel they represent value that can then go onto LinkedIn, and the other social networks.

    Things are definitely building in my mind; and projects are coming along. However, still a long way from anything concrete taking off.

    For the moment would just like to talk about MCP, because it really is a very significant milestone in the AI world. (Image context from OpenAI was another one recently).

    MCP

    Model Context Protocol was announced in Nov 2024 by Anthropic.

    This is a big step forward since it has standardised how language models can/should interact with external tools.

    In order for LLMs to be further useful, they need to be able to action things.

    I’m guessing that once Anthropic had integrated a couple of tools with their LLMs they realised it would be better to have a standard way of doing that … and from that they came up with the MCP architecture.

    At first understanding, giving a language model a standard way of interfacing with the outside world is a bit of a game-changer and there are already a ton of integrations that we can use straight away. Things are happening really fast.

    Core Architecture

    Essentially, you have an application (called the Host), and this has a Client inside it.

    The Client maintains connection to the Servers which have access to the tools you want the LLMs to connect to.

    Protocol Layer

    It’s fairly simple … you have:

    • Requests
    • Notifications
    • Results

    You have functions that:

    • Handle incoming requests
    • Send requests and await response
    • Handle income notification
    • Send a notification

    Transport Layer

    All transport layers use JSONRPC

    JSON-RPC Request Object

    • jsonrpc : always going to be “2.0”
    • method
    • params (optional)
    • id

    Without an ID, it is considered a notification that doesnt expect a response. In fact, servers MUST NOT reply to a notification.

    JSON-RPC Response Object

    • jsonrpc (version string)
    • result
    • error
    • id

    To be clear, MCP is a standard way – a proposed specification that multiple parties can agree on – for how language models will interact with outside tools.

    Protocols are vital in tech. We have TCP-IP and email, which you use all the time. Without that agreement, we could have had an incredibly fractured internet.

    When companies and developers can agree to do things in a certain way, it makes it easier to make systems.

    Using MCP I assume is very much like working with interfaces. If you code an LLM up to work with your own tools and then for whatever reason you decide to switch LLMs … then using a protocol would mean there’s no lost work – because the new one will use exactly the same interface as the current one.

    Reference

    4 Request object

    A rpc call is represented by sending a Request object to a Server. The Request object has the following members:jsonrpcA String specifying the version of the JSON-RPC protocol. MUST be exactly “2.0”.methodA String containing the name of the method to be invoked. Method names that begin with the word rpc followed by a period character (U+002E or ASCII 46) are reserved for rpc-internal methods and extensions and MUST NOT be used for anything else.paramsA Structured value that holds the parameter values to be used during the invocation of the method. This member MAY be omitted.idAn identifier established by the Client that MUST contain a String, Number, or NULL value if included. If it is not included it is assumed to be a notification. The value SHOULD normally not be Null [1] and Numbers SHOULD NOT contain fractional parts [2]

    The Server MUST reply with the same value in the Response object if included. This member is used to correlate the context between the two objects.

    [1] The use of Null as a value for the id member in a Request object is discouraged, because this specification uses a value of Null for Responses with an unknown id. Also, because JSON-RPC 1.0 uses an id value of Null for Notifications this could cause confusion in handling.

    [2] Fractional parts may be problematic, since many decimal fractions cannot be represented exactly as binary fractions.

  • Day 57 – The Habits Ahead

    Strong results come from doing the right thing many times over.

    Consistency. Discipline.

    I’ve got multiple projects going on at the moment and they can easily fill up my days entirely, without time for the things that will necessarily continue to build the business. But I am dropping things that will actually continue to build the business.

    These are a good additional starting point to my existing habits:

    • Grant and funding research & application
    • Event, conference and networking research
    • Email marketing pipeline
    • Website marketing
    • Social media marketing

    It’s a bit like programming yourself for succeeding; you decide what actions the business would benefit from; or what areas need to be attended to consistently.

    These become the KPIs of your business.

  • Day 56 – Pandora’s Box.

    I think the way the world is right now, people need to feel like there is a new wave of ‘something good’ … and whilst AI is certainly going to take many jobs, it opens up a whole world of possibility for those who are prepared to learn about it, work hard and creatively use it. So there has been this huge positive wave of energy (certainly amped up with money) toward innovation. It may well be that LLMs have limitations, and that we are witnessing a bubble… but people are now TRYING new things. Technical and non-technical people are finding they can do a whole lot more using AI. What I mean is, a lot of crazy ideas are being unlocked. Pandora’s box has been opened.


    True Personal Assistants

    For me, it gives me the opportunity to build a personal assistant of the magnitude that i’ve wanted to few decades. I think the science fiction movies and video games influenced me in wanting to build these personal assistants.

    Examples are

    JARVIS Assistant From Iron Man Movies

    J.A.R.V.I.S., which stands for “Just A Rather Very Intelligent System,” is Tony Stark’s natural-language user interface computer system, named after Edwin Jarvis, the butler who worked for Howard Stark and the Stark household.18 Initially, J.A.R.V.I.S. was developed as a simple AI assistant to control Stark’s Iron Man suit, but it evolved into a powerful AI capable of managing various tasks and assisting Stark in his personal and superhero life.51 J.A.R.V.I.S. uses advanced natural language processing and communication skills to understand and respond to Stark’s commands, making it more than just an AI tool—it’s a trusted companion.

    So, the ‘trusted companion’ thing here is the key.

    Cortana from Halo

    As an artificial construct, Cortana has no physical form or being. Cortana speaks with a smooth female voice, and projects a holographic image of herself as a woman. Cortana is said to resemble Halsey, with a similar attitude “unchecked by military and social protocol”. In Halo: The Fall of Reach, Cortana is described as slender, with close-cropped hair and a skin hue that varies from navy blue to lavender, depending on her mood.[6]: 216  Numbers and symbols flash across her form when she is thinking.[9] Halsey sees Cortana as a teenage version of herself: smarter than her parents, always “talking, learning, and eager to share her knowledge”.[6]: 218  Cortana is described as having a sardonic sense of humor[10] and often cracks jokes or wryly comments, even during combat.[6]: 217 

    There’s a few more but that’s enough for now

    So, the key components are:

    • LLMs (Language Models)
    • Decision Trees
    • GenerativeUI
    • Automation flows
    • Data Mining & Analysis
    • Context and Object Oriented Memory
    • MCP

    In the future I imagine, the internet has pretty much been abstracted from us. Which isn’t a great thought for most of us, but it’s kind of a natural progression. Things change, for instance it was naive of me to think that I could have another 20 years of making money doing the same thing until I retire i.e programming.

    At school, I had a maths teacher who used to program in assembly. Talk about talent… in a few decades will we have any humans that know how to write in assembly? I always wondered why he didn’t still program, and it was probably because he didn’t move on.

    In the same way, those of us who understand HTML/CSS/JS on a deep level … these skills are fast becoming abstracted away with vibe coding apps. The vibe coding apps that combine with Supabase are going to get better and better; but will the prediction machine ever get so good that it can really understand programming so that it makes it super clean (probably yes, as our prompt engineering gets better to help it). Eventually, the personal assistants will replace any requirements that vibe coding creates now.

    The next big waves/industries to come along will be more widespread autonomous machines (drones, robots, cars), surveillance and wide scale sentiment monitoring; then beyond it some sort of biohacking industry specifically with longevity at the core; video games will have a resurgence in originality; so I do hope that something will replace the employment drop-off that will shortly happen.

    That’s enough thoughts for today.


    Work Updates:

    • I continue to work on a side project to bring in a small amount of money but enough to cover personal costs. It’s a fun, high potential project, and made me realise I do like working on IT dev projects, and am quickly improving at the management of them.
    • The DXP project is slowly getting to the beta launch was is great. Once that’s hit, I will share more details.
    • My own platform is coming along. Shown it to a few people and I know these people wouldn’t sugarcoat stuff, it’s super days but reasonably positive. Just got to keep going.
    • The ideas that would drive an AI Agency are coming together; but still a little way off from doing that
    • Continually trying to keep up with the industry
    • More and more aware of the necessity of marketing for everything

  • Day 52 – ‘Flowgramming’ as the new threat and opportunity to programmers

    So, the new era of the internet is going to be connecting the dots. In this paradigm, the dots are the ecosystems that already exist and have many users.

    These ecosystems have had millions of dollars poured into them. They have somewhat of a technical moat now but their user ecosystem widens that moat most of all.

    For instance, I have been meaning to get started on N8N for a few weeks now. I’m glad I sat down and made time for it today … this is a brand new software type available to people without having to pay for the upfront development of handling the communication between the dots.

    As a starting point I setup a workflow that grabbed my favourite Arsenal FC blog, asked OpenAI to summarise it, and then message me them on slack.

    After some fiddling around I got some messages in Slack!

    I can’t overemphasise how great this is. This is a huge timesaver, but it’s yet again another threat to programmers.

  • Day 51 – Vibe Coding

    Vibe coding is really taking over. Simply put, non-technical people with ideas can now use AI to make very high quality prototypes.

    Those prototypes can do most of the things that are needed for a company. However, in almost all cases, the code generated to run them is somewhat bad (from a software engineer process) … and if you keep going down that route without regular refactoring and tidying up, eventually you will get technical debt.

    That technical debt may come three years down the road though, at which point you will have to hire an experienced programmer. If you are still going in three years though you are likely to have money by that point and can afford the programmer. So that’s the payoff!

  • Day 50 – Building An AI Startup In Six Months

    Day 50 – Building An AI Startup In Six Months

    I’m not a fan of the new image generation on ChatGPT. Not because it’s more detailed or more refined – the quality has gone up … but the fun images that I was able to generate for my posts quickly don’t seem to exist anymore. The new images take several times longer to generate, but I’ll admit they are much higher quality.

    I also tried out the new video creation on GenSpark from a prompt from one of my previous posts. Whilst its a low quality video, you can clearly see a future where we can use AI to construct our environments visually in ways that have never been possible before. This is the video I generated below.

    So fifty days have passed since I made a commitment to building ‘something’ with AI. I have roughly six months until a self-imposed deadline at which point I will properly reassess my situation. I am totally aware that the industry is incredibly competitive, and I’ve realised that more than ever, the real route to success is marketing.

    But for marketing to be effective, and not to be run over by OpenAI … you need a specific niche, sector, problem or new value proposition because you can then use highly targeted marketing and lead generation for your product. If you have a general purpose product, you will get lost in the noise. So specific problems taken care of, mean you can target your marketing and sales. You can also tailor your product.

    I didn’t initially have a specific idea … but having worked with SMEs for my entire career, I do know what business owners are looking for. There are already infinite tools out there but I wanted to build something in my own way and own style. It sounds cheesy, but web software has always been my ‘passion’ so this was more about one final attempt to take everything I’ve learnt so far and put it into one system.

    I have begun a custom objects platform that I’ve wanted to build for a while – which I think can form the digital infrastructure for several niches, and ultimately the long term goal is to build my own flavour of AI assistant. Whilst this may seem wrapped up already, my experience tells me to build something that’s going to be useful for myself and people around me, and evolve it as I learn about the requirements that crop up as a result of pushing the AI agency. As the industry quickly evolves, it’s mostly going to be about persistence and spotting opportunities.

    Aside from my own product, I’ve been exploring a partnership with an AI platform focused on the online gambling sector, starting the foundations of an AI agency to see what that entails and now more recently I’ve taken on board an online booking system transformation. This is all on top of jumping into the AI R&D scene, learning about the various techs.

    So I’ve been quite busy, and it’s an enjoyable life really. I much prefer it to being a developer with no real impact or choice about what they get to work on. I’m aware I need to make money with my product at some point, but for the moment I’ve got a few steps left on the platform that I want to get done to get it to a point where I can apply it to different use cases.

    The main thing I have learnt about in the first 50 days is how powerful LinkedIn can be if you were to work at it. I haven’t had too many likes on my posts, which I never really expected anyway since I started off with less than 100 contacts and the content was more for personal motivation than anyone else … but I have had some key things come up from it … people from my past have messaged me, and I’ve been able to reconnect to them and talk about what i’m doing. And when I want to a local conference, I was surprised by how many people were aware that I was at least doing ‘something AI’ related.

    The point is, the last 50 days has made me realise that if you work LinkedIn properly you can always find work wherever you are in the world, regardless of industry.

  • Day 49 – Productive Conversations?

    After talking about protecting my time yesterday; I went and spent an entire day talking to people today.

    Was it productive?

    When focused on building stuff everyday, you need to balance it out with social contact. It’s good to laugh, to share ideas, and to get other peoples’ perspectives on life and AI. It’s good to get away from the screens that suck our lifetime away without us noticing.

    There is a network effect to it as well. You talk to one person, make a positive impression. Maybe something that they are looking for you, you can help with… or you link them to someone you know. Or vice-versa. You never know what the ripples of action will touch in reality.

    That’s all for today.

    Tomorrow back to action getting stuff done …

  • Day 48 – Protecting Your Time

    To achieve great things, two things are needed; a plan, and not quite enough time.

    Whilst I’ve known this for a while, it’s been more apparent in this startup attempt, of the importance of protecting your time.

    The unfortunate truth is that all of us have less time in life than we would like to. You never really know when your time is going to be up and most of us project our consciousness into the future, never really taking stock of the life we literally are right now.

    Whilst I do get occasionally stressed and frustrated at work, I do genuinely enjoy doing what I do. I always have. Maybe I would enjoy other engineering careers, but internet & software is where I put the last 30 years of my life so there is a lot of sunk cost bias.

    I also genuinely enjoy being self-employed and having my freedom of choice to make decisions (even those that lead to mistakes) and life freedom in general (as opposed to a nine to five). I certainly could have had an easier and more profitable life so far if I had taken the employee route but then I wouldn’t have had all the life experiences i’ve had.

    The point is, when you are doing something that you enjoy, by default your time is reasonably well invested. If you are on the right path for you, then regardless of how inefficient or ineffective you are, fundamentally you are still making good use of your time.

    It’s when you really don’t like what you’re doing, or where you are going to work in the morning hating the prospect, that you are wasting time.

    And fortunately, right now, I am on the right path for me. So that means at least at the base level my time is being invested in the right area.


    However, for the days 50 to 100, I want to up my game considerably. This doesn’t mean running around in circles faster … it means:

    • doing less (paradoxically)
    • doing more (paradoxically)
    • automation

    Doing Less By Saying No

    There is one rule here. It is about saying NO with wisdom. Saying NO to the unimportant is the one thing that I want to implement. Whether thats a phone call, a meeting, a project idea, attempting to nurture a friendship, a task, social media, etc. Saying no to an unwarranted impulse to check email or social. But for me especially as someone who can generate a new idea every hour, it’s about saying no to those and staying focused on what I need to get built.

    Doing More By Focusing On Key Areas

    The rule here is to systemise. I hope eventually to be in the position to build a team, but in reality it’s about optimising myself first. Having said no to the non-important by doing less, you do more by consistently focusing on key areas. As an example, LinkedIn is a key area. I’ve dabbled in it for the last 50 days for the first time taking it somewhat seriously, but I also know that I am not optimising each post to deliver value to others. So that has to change.

    Well Thought Out Automation

    More on this another day…

  • Day 47 – Pattern Recognition, AI, and the Nature of Intelligence

    Day 47 – Pattern Recognition, AI, and the Nature of Intelligence

    Our brains are fundamentally pattern-recognition engines. Evolved for survival, they identify regularities in our environment to make better predictions and decisions. Learning is a process of repetition and refinement: every time we encounter a situation, we subtly recalibrate, inching toward optimal responses. It’s like an organic algorithm optimizing itself with each iteration.

    So basically, part of our intelligence is how we recognise patterns as we go through life. It was a matter of survival many years back. Eat that red berry and you get sick, stamp on that snake and it’ll bite you… but more than that … what about sales psychology … use these evocative words, anchoring, mirroring, and positive affirmation statements … and you are more likely to get more sales. So patterns have already been assessed.

    Science is literally built on patterns. The scientific method is about repeatable and measurable tests. You look for patterns in data to assess whether an hypothesis is correct or not.

    So …

    This idea leads naturally into how Large Language Models (LLMs) work. At their core, LLMs are also pattern recognizers. They don’t understand meaning the way we do — instead, they analyze massive amounts of text and learn to predict the next word based on patterns they’ve seen before. They don’t “know” facts; they generate statistically likely sequences. Think of them as highly advanced autocomplete systems trained on a trillion examples.

    Whilst LLMs aren’t fully intelligent… if we were ever going to reach genuine AI … this pattern matching ability is very much part of that. Who knows what will happen with the quantum chips… since what does ‘meaning’ mean anyway? AI can ‘know’ everything about a dog, what it looks like, sounds like, how it behaves, etc… maybe it can’t ‘experience’ a dog … but it certainly conceive of one.

    This gives rise to the thought that we have created something potentially very different to us, but that is still intelligent but in its own way. Our human ego don’t want to admit that this ‘thing’ may have more intelligence, so we have that internal roadblock… but why are we so fixated on it achieving ‘intelligence’ anyway – AI doesn’t need to be intelligent to be self-organising.

    While LLMs are masters of mimicry, human intelligence is something deeper. Intelligence isn’t just about spotting patterns — it’s about applying them to adapt, solve problems, and pursue goals in unpredictable environments. It includes:

    • Perception (pattern recognition)
    • Learning (memory)
    • Reasoning (applying knowledge flexibly)
    • Agency (goal-directed behavior)
    • Generalization (using insights across domains)

    LLMs replicate the appearance of intelligence — eloquent, insightful, even persuasive — but they lack goals, memory of past interactions, and any sense of self or purpose. They are mirrors of the data they’ve consumed, not minds of their own.

    It can easily be argued, and has been by philosophers over the years, that the human animal is nothing more than a biological robot… that many of us go through life without ever truly thinking; instead just riding the waves of the mind; which of course is just made up of what it has learnt over time.

    True intelligence, especially human intelligence, combines cognition with emotion, instincts, and an internal drive. It adapts to change, grows from failure, and learns not just how to do something — but why it matters.

    So what is intelligence? It’s not just knowing things. It’s the ability to use patterns in creative, adaptive, purposeful ways. LLMs are incredible tools. They’re not alive, and they’re not wise. But perhaps AI doesn’t need to be ‘alive’ and ‘intelligent’ to be an existential threat to us. I’m not saying LLMs are Terminators … but I am saying they are part of them. It just needs a few more technical breakthroughs combined with them, and then it’s time to reach for the EMP grenades.

  • Days 45 & 46 – Weekend off.

    Apart from a two hour call covering some booking system improvements; I took the weekend off. Mothers day on Sunday as well so bit of a BBQ and kicking a football about the park.