Agreed. There shouldn't be any expectation at the very early stage for a disruptive tech company. Even 2034/35 seems a bit too early to expect any steady stream of revenue. I am happy to add another 5 of 6 years on top of that to give them a chance to double the current share price.
My point was that there has always been some sort of expectation for the revenue ...... it's not a brand new idea that a business generates revenue... actually, the expectation was there even before Akida....... I even remember back in like 2018 or before people were wondering whether we r in Amazon Alexa lol...
Manny to call this a "gaffe" is an understatement.
Focus on the gaffes and you miss many big picture positives such as:
Thanks FK,Lumi still offer the roadmap presentation if you cant wait for the release on the website.
BrainChip Technology Roadmap Presentation
View attachment 84062
It seems like the 'engine bay' is NOW ready for customers to play with, tweek and test out our wares in relation to their products.
Availability of model support and simpler set up will facilitate faster processes for the end user.
We seem to have been feeding them fish until now, we are offering them to learn how to fish going forward. Less 'hand holding'.
9 million bookings this year for the CEO as a KPI will be a good thing to see.
One might think employees are set achievable targets some might receive stretch targets I hope this is the former.
One would think 9 million is small change for this kind of tech but any money entering might be the ignition source we need.
I'm looking forward to seeing the changes to the web site which were also mentioned.
From cold dead hands I remain
Top 20 Most Popular State Space / Sequential Models (Including LLMs & Modern Hybrids)| Rank | Model | Type | Use Cases |
|---|---|---|---|
 | Large Language Models (LLMs) | Deep Learning | Chatbots, agents, assistants (GPT, Gemini, Claude) |
 | Kalman Filter | Linear-Gaussian | AR/VR head tracking, IMU fusion, finance, GPS |
 | Hidden Markov Models (HMMs) | Probabilistic | Speech recognition, gesture tracking, NLP tagging |
 | Extended Kalman Filter (EKF) | Nonlinear | Drones, autonomous navigation, smartwatches |
 | Particle Filter | Nonlinear, Sampling | AR/VR tracking, robotics, IoT localization |
 | Unscented Kalman Filter (UKF) | Sigma-point | Automotive ADAS, wearables, inertial sensors |
 | Structural Time Series Models (STSMs) | Interpretable | Power grids, IoT energy sensors, economic forecasting |
 | Dynamic Linear Models (DLMs) | Bayesian | Sales forecasting, wearables, health analytics |
 | Switching State Space Models (SSSMs) | Regime-switching | Fault detection in IoT, market phase modeling |
 | Deep State Space Models (e.g., RNN-SSM) | Deep Probabilistic | AR gesture prediction, speech synthesis, biosignal modeling |
Emerging or Specialized Models (Ranks 11–20)| Rank | Model | Type | Use Cases |
|---|---|---|---|
| 1 | Neural State Space Models (NSSMs) | Deep Neural ODEs | Physics-informed wearables, prosthetic control |
| 1 | Variational State Space Models (VSSMs) | Bayesian Deep Learning | EEG/fMRI analysis, AR/VR cognitive load estimation |
| 1 | LSTM State Space Hybrids | Seq2Seq + State | Smartwatch motion prediction, anomaly detection |
| 1 | Transformer-based SSMs | Attention with dynamics | Real-time AR/VR captioning, gesture translation |
| 1 | Nonlinear Autoregressive Exogenous Models (NARX) | Time Series | IoT weather sensors, predictive maintenance |
| 1 | Bayesian Filters (non-Kalman) | Probabilistic | Smart meters, context-aware wearables |
| 1 | Time-Varying Coefficient Models (TVCMs) | Dynamic Regression | IoT sensor drift correction |
| 1 | Reservoir Computing (Echo State Networks) | RNN-like | Low-power IoT edge devices, wearables |
| 1 | DeepAR (Amazon) | RNN Forecasting | Smart energy usage forecasting |
 | Spatio-Temporal State Space Models | Multivariate | Environmental sensor networks, smart cities |
SummaryI think they realize that Edge Impulse is no longer going to support them and they need their own developer platform.
semitechsemi.com
Thanks Tech, my take below,Good morning,
Look, some criticism is justified in my opinion during different stages of our development, from poor communication, poor management of
our funds, poor business planning, as in, not addressing traction issues early enough, while funds kept being pumped into sales teams who
clearly couldn't sell the idea, which I believe was a management issue myself, and possibly internal staff accountability issues etc.... but I
would suggest that many young start-ups battle similar growing pains.
I have been onboard for just shy of 10 years, and currently have a sense of calm surrounding this investment, are we in the best position
we have been in for years? yes, I believe so, a lot has been to do with education, not only of the semi-conductor industry or companies
actually, genuinely wanting to understand the real benefits of SNN technologies, but also our company as a whole, it's been a real learning
experience, which all groups appear to be coming out the other side of into clear air.
Peter's years of hard work and knock backs clearly show just how far advanced his research was, hence, we appear to be coming to that
inflection point, the bridge that was once too far, feels like that same bridge is close to completion as we as a company claim our share
of this new frontier, there's plenty to share around, our share of the pie will be huge moving forward I'm convinced of that, my opening
paragraph was fair I believe, but I, like many long termers have moved on, learnt from those early errors (growing pains) and can finally
see a horizon with a bright light emerging, our day is closing in, Brainchip will be successful, stay the journey with me and many others,
we all deserve to rise up as one!
Love our company.... Tech.
