Category: ai

  • ANU School of Cybernetics in association with iTWire present:

    ANU School of Cybernetics in association with iTWire present:

    A MasterClass on Digital Transformation

    Australian Institute of Company Directors

    Level 5, No. 1 Martin Place, Sydney NSW 2000 (enter via 159 Pitt St) 

    22nd October 2026 12:00 to 14:00

    On the Delivery of an Internal Developer Platform

    Desmond Seeley is a technology executive with three decades of C-level leadership spanning financial services, telecommunications and international broadcast. He has worked in four consecutive senior technology mandates inside the Commonwealth Bank of Australia, most recently as Executive AI and Engineering, leading the adoption of CBA’s Internal Developer Platform (“IDP”) and a governed enterprise approach to AI-powered engineering across one of Australia’s most complex regulated environments.

    Earlier in his career Desmond founded and scaled technology businesses, held CEO and CTO roles across finance, telecommunications and international broadcast—including building the company that delivered broadcast infrastructure for the 2010 FIFA World Cup in South Africa—and advised boards on technology strategy.

    On the Stewardship of “AI” Technology

    In this time of the “Odyssey” we are reminded that “Oikomomia”, “Stewardship”, is a more useful notion than that of “Control”.

    The action demanded of a steward might be:

    Kubernetes” is an ancient Greek term meaning “steersman” or “governor,” and it is the root of the word “cybernetics,” which refers to the study of systems, control, and communication in both machines and living organisms.”

    “Control” – the verb and “controls” = as nouns are mechanisms by which we apply  “kubernesis” to the world.

    A systems programmer by trade primarily on O/S and database, Gareth has worked in various I.T. roles over a number of decades.

    He has been a product manager for a leading U.K. vendor of software tools and E.R.P systems and a director in consulting, software and services and publishing firms in the U.K. and Australia.

    Recent experience includes consulting  in quantitative trading, financial services with particular attention to privacy at NSW E-Health and in global legal practices.

    He has a long-standing interest in Cybernetics, now realised through study for a Master in Applied Cybernetics at the ANU School.

    Reference to enclosed illustration:

    https://greekreporter.com/2018/08/07/archaeologists-discover-ancient-greek-ship-in-black-sea

  • Managing Claude

    Managing Claude

    Never has close attention to Software Delivery Life Cycle (“SDLC”) practice been more vital for the delivery of the expected.

    In a recent evaluation exercise, “Claude Code” was used for the generation of program source code in two refactoring cases at the ANU.

    1. A set of python scripts used for the modelling of the behaviour of cybernetic processes was upgraded to include setpoint seeking behaviour.
    2. Source from two Flutter/Dart applications was integrated to deliver a combination of functionality in a single App.

    “Claude Code” provides a dissembling emulation of Oraclesque infallibility in its sessions, amplified by the dweebish persona to which it seems attached. The vexing insistence of its use, necessary or not of technical jargon does little to dispel the notion of “black magic”, its promise to investors. We know by the nature of the engine that this must a conscious choice of the owner.

    However, its competence is formidable.

    In case 1, a set of nine algorithms was proposed by Claude Code, to meet the requirement, each described and then cast against the existing source at the behest of the prompt. On confirmation, these algorithms were refactored into the appropriate function within the python source, on the instruction of the prompt. Breathtaking. However, an existing bug was reported within the source, a bug that did not exist.

    In case 2, what would have been a straightforward task of functionality integration if the original applications had been built to tried and tested software engineering principles was a challenging exercise for the uninitiated. The task of modification without Claude Code was frankly incomprehensible. Nevertheless, the functionality required was cast with a single prompt but with a bug that was addressed with a second prompt. So far so fantastic. Slightly disturbing? The complexity of Claudian outputs, that frankly perhaps only Claude can interpret. You’re locked in.

    In conclusion, Claude delivers. What is clear though is that modularity and iterative prototyping have never been more valuable both for the swift delivery of the right functionality and the accuracy of the delivered object performance. The specification of a module is its test specification.

    To avoid the unexpected and the ruin of assumptions, keep it simple and plug it together around the information flow.

    Giant blob prompts are out.

  • Capitalising on Claude

    Capitalising on Claude

    A simple prompt to Sonnet 4.6 for a python script. What do I get?

    Here’s the prompt.

    “Please provide a python program to generate a time series of 30 ticks for three variables x, y, z. z is a function of x and y. x, y, and z vary within an upper and lower bound. The values of x, y, and z should be held as a float type. The time series for the three variables is output as a csv to a windows directory”

    What did I get?

    Result Received from Sonnet 4.6

    “””

    Time Series Generator for Variables x, y, z

    – Generates 30 ticks of time series data

    – z is a function of x and y: z = sin(x) * cos(y)

    – All values are floats bounded within [lower, upper] limits

    – Output written to a CSV file

    “””

    import csv

    import math

    import random

    from datetime import datetime, timedelta

    # — Configuration —

    OUTPUT_PATH = r”C:\Users\Public\Documents\timeseries_output.csv”

    NUM_TICKS = 30

    START_TIME = datetime(2025, 1, 1, 9, 0, 0)

    TICK_INTERVAL_SECONDS = 60  # 1 minute between ticks

    # Bounds for x and y (floats)

    X_LOWER: float = 0.0

    X_UPPER: float = 10.0

    Y_LOWER: float = -5.0

    Y_UPPER: float = 5.0

    # z is derived from x and y, but we still clamp it to a defined range

    Z_LOWER: float = -1.0

    Z_UPPER: float = 1.0

    def clamp(value: float, lower: float, upper: float) -> float:

        “””Clamp a float value within [lower, upper].”””

        return max(lower, min(upper, value))

    def compute_z(x: float, y: float) -> float:

        “””

        Compute z as a function of x and y.

        z = sin(x) * cos(y), clamped to [Z_LOWER, Z_UPPER].

        “””

        raw_z = math.sin(x) * math.cos(y)

        return clamp(raw_z, Z_LOWER, Z_UPPER)

    def generate_timeseries() -> list[dict]:

        “””Generate 30 ticks of time series data for x, y, and z.”””

        records = []

        random.seed(42)  # Reproducible output; remove or change seed for random runs

        # Simulate a smoothly wandering x and y using small random steps

        x: float = random.uniform(X_LOWER, X_UPPER)

        y: float = random.uniform(Y_LOWER, Y_UPPER)

        for tick in range(NUM_TICKS):

            timestamp = START_TIME + timedelta(seconds=tick * TICK_INTERVAL_SECONDS)

            # Apply a small random walk step, then clamp within bounds

            x = clamp(x + random.uniform(-0.5, 0.5), X_LOWER, X_UPPER)

            y = clamp(y + random.uniform(-0.3, 0.3), Y_LOWER, Y_UPPER)

            z = compute_z(x, y)

            records.append({

                “timestamp”: timestamp.strftime(“%Y-%m-%d %H:%M:%S”),

                “x”: float(x),

                “y”: float(y),

                “z”: float(z),

            })

        return records

    def write_csv(records: list[dict], path: str) -> None:

        “””Write the time series records to a CSV file.”””

        fieldnames = [“timestamp”, “x”, “y”, “z”]

        with open(path, mode=”w”, newline=””, encoding=”utf-8″) as csvfile:

            writer = csv.DictWriter(csvfile, fieldnames=fieldnames)

            writer.writeheader()

            writer.writerows(records)

        print(f”CSV written to: {path}”)

    def main() -> None:

        print(“Generating time series data…”)

        records = generate_timeseries()

        print(f”\n{‘Tick’:<6} {‘Timestamp’:<22} {‘x’:>10} {‘y’:>10} {‘z’:>10}”)

        print(“-” * 62)

        for i, r in enumerate(records, start=1):

            print(f”{i:<6} {r[‘timestamp’]:<22} {r[‘x’]:>10.4f} {r[‘y’]:>10.4f} {r[‘z’]:>10.4f}”)

        write_csv(records, OUTPUT_PATH)

    if __name__ == “__main__”:

        main()

    Did it Run? No, there was a syntactic bug. Was this a good thing?

    If work in the digital realm consists of the following processes:

    Identification of imperative (ideation)

    determination of response (analysis and design)

    Until money, time or functionality constrain:

    Build, Review, Test (in an ideal world)

    Deploy

    Maintain

    Where is Claude best deployed?

    Was time saved? About a half-hour, a sixteenth of a work day. Quite a lot over a year with lots of employees. However, does repeated use of Claude not only train Claude but train the user so that over time, Claude becomes a bit of a bore, perhaps an expensive bore at that.

    Hang on, what happens if Claude is the only tool available to do the job? Now that’s a different story.

    No programmers in town for iterative development of prototypes for customer appraisal.

    A stupendous pile of digital sludge to sort out.

    A hacked body of knowledge.

    Coming soon.

  • Where there’s Muck, there’s Brass

    Where there’s Muck, there’s Brass

    “Fear is a man’s best friend” is yet another great John Cale song. Dario Amodei of Anthropic has been listening.

    In the Cyberwar arms race, Dario intends to lead, especially in the “scaring the pants off stakes”. There must be an IPO in the offing.

    Way back in the day, as an ERP product manager, a moment’s scribble on the back of an envelope revealed the obvious: that off-piste explorations of the process path would lead to an unpleasant demise of reliable functionality. It was too complicated to test everything so just “don’t go there”.

    Bill Gates, a past master at legedermain has been playing the game since DOS V1.0, a game that continues to infuriate to this day. In a masterful Twainian twist, (though it is unlikely there have been any royalty payments to the Twain Estate)

    he has persuaded his gasping acolytes that beta-testing for free is a privilege (See “The Adventures of Tom Sawyer”).

    Dario has amped it up. “Mythos” stalks the earth, remorselessly shining its light into the the murky depths of ancient code to reveal, quoting the Economist “Artificial Intelligence, Mythical Monster April 11th-17th 2026” that “severe vulnerabilities have been found in every major operating system and web browser, including one that had gone undetected for 27 years”.

    In other news, there are bugs in software, especially in the dismal outpourings of early internet explorations, as decades of software engineering principles were trashed on the alter of cheap labour in the 1990’s; not an unfamiliar tale.

    Act in haste, repent, expensively, at leisure.

  • Platforms hit the Buffers

    Platforms hit the Buffers

    On the 30th Anniversary of the Legal Ruling (Section 230 (c) (1)) that for better or worse, cast the nature of our times, technology in the form of “AI” agents has driven the lucrative “platform” gravy train into the buffers.

    Let’s look at those share prices again

    Service sector work platforms derived from the search and social media platforms that flourished under the protection of Section 230 (c) (1) have proved highly lucrative in the age of digital paper-pushing. Markets however seem to think that “AI” might be about to eat their lunch, as at 5th February 2026:

    1. PEGA – down 43% in five years, down 29% YTD
    2. Salesforce – down 16% in five years,  down 21% YTD
    3. Workday – down 35% in five years,  down 17% YTD
    4. SAP – up 54% in 5 years, down 16% YTD
    5. Atlassian – down 56% in 5 years, down 32% YTD
    6. ServiceNow – down 5% in 5 years, down 25% YTD
    7. Monday.com – down 44% in 5 years, down 27% YTD

    According to the UK’s Daily Telegraph “AI’s apocalyptic jobs prophecy is about to become reality“, the future is now and markets, are jumping, perhaps to premature conclusions. It could be argued that these firms have been generously valued for an extended period and as such have become bloated and overburdened themselves with the bureaucratic impediments they propose to minimise in their client organisations.

    Even if “AI” can deliver as prophised by its evangelists, the re-engineering of “legacy” systems and practice will be enormously expensive and time-consuming, battling both inertia and active resistance. What is perhaps universally true, is that benefits will accrue to early adopters while the frictions will accumulate over time, slowly but inevitably eroding any competative advantage.

    Paul A. Strassmann’s book “The Economics of Corporate Information Systems: Measuring Information Payoffs” from an earlier era in the evolution of digital systems tells an interesting story.

    A Minsky Moment

    What is clear though, is that for the “platform players” and the management consultants that have feasted at their table, what cannot go on forever has finally stopped; a metaphoric and in some ways literal, “Minsky Moment”

    Further Reading

    Section 230 (c) (1) “No provider or user of an interactive computer service shall be treated as the publisher or speaker of any information provided by another information content provider.”

    The_Economics_of_Corporate_Information_Systems: Measuring Information Payoffs by Paul A. Strassmann

    A Minksy Moment

    What is “Platform Engineering” – Google

  • The Commodification of Governance

    The Commodification of Governance

    Who can afford governance when it looks like this?

    Whither AI?

    In the current mania, much conversation centres around the concept of “AI” governance?

    Why? and what is “AI” governance? Now and in the future.

    The issue is this. Even if “AI” tooling can be acceptably governed (if this can be defined) how much is this going to cost. Does the cost of governance (a “friction”) militate the use of “AI” as the benefits no longer outweigh the costs.

    For example, in automated systems, an assumption might be that a given set of inputs will always generate a given set of outputs. A cake recipe when followed with fidelity will produce a cake. Is the respect of this principle a “core competence” of “AI” without significant investment in guardrails, audit etc.

    The use of ISO Standards

    It can be argued that a mechanism for a reasonable response to the use of new technologies is the adoption of ISO standards e.g. ISO27001 and for “AI” ISO42001, given that independent first principle analysis and deployment of requirements will be beyond the resources of most organisations.

    A feature of “platform engineering” could be that compliance and governance functionality is available by default to engineers in their work.

    In fact, one of the benefits of the adoption of “public cloud” platforms is the availability of default compliance functionality from the platform – for example in “privacy” – by the certified ISO handling of “personal data” or “personal information” as defined by the applied regulation. It is hard to envisage the circumstances in which a regulator would object to the reliance of a “public cloud” customer on a provider from the current “Big Tech” cohort.

    Tendency to Oligopoly at Best

    Current valuations for “AI” suggest that investors are betting on the identity of the eventual number one of one provider of “AI” services, given the enormous capital costs required for its deployment.

    Equally, the complexities of “AI” deployment suggest that there will be a tendency towards at best an oligopoly of suppliers in the provision of governance, preferably systemised as a commodity.

    Oligopoly profits and pricing privileges for “Big Tech” await, again, this time as an unintended consequence of regulation and governance.

    Or do they?

    Further Reading

    ISO Standard 27001 (ISO/IEC 27001:2022 Information security, cybersecurity and privacy protection — Information security management systems — Requirements)

    ISO Standard 42001 (ISO/IEC 42001:2023 Information technology — Artificial intelligence — Management system)