Yeah, it's becoming challenging. A lot of that is because talent acquisition teams are either too heavily reliant on poor automation or they are the bottleneck in communication because of the application volume. Trying to fix that by making responses more deterministic and a first-class citizen. So if you are using a claw or Claude to apply, you get the ability to poll over the status yourself in a native way.
Ah, yeah. This is definitely more focused on creating structure to help reduce the "ATS black hole" problem as well as solve for candidate quality signals. It's anticipating headless ecosystem for hiring humans.
It's less about "AI said this guy" and more about proof, identity, and hopefully making getting seen as a candidate easier. Right now the signal/noise problem is progressively making that more difficult.
We have seven other companies involved that are listed on the website and have provided feedback for general use-cases -- given adoption curves are super hard, early traction we have around OJCP is encouraging to me. A lot more to do, but any help or ideas are greatly appreciated.
I'm chatting with other OSS organizations to help make sure this lands in no specific organizational control.
I value the effort to standardize things and make them consistent, benefits everyone. I do appreciate you for taking lead here. However more than objection my worry is about the "PROTOCOL" key word being diluted. Imagine if everyone runs with it to solve their motivated use cases.
Adding to the topic rather than dog piling onto the website critique:
I think "the context" the author defines also includes harder to unlock peripheral components that makes software products in 2026 defensible.
For example, security is getting harder not easier. Enterprise trust rides on needing to have up-to-date knowledge on data regulation as well.
Relationships, brand maturity, discovery; all of that becomes rarified as we continue to reduce the actual product launch curve -- but also the "harder to do" hygiene stuff that takes you from a "I vibe coded this in two days, good luck" to a "your successful company can count on us" type of hardening has become progressively more of a baseline requirement.
In a way, this new dynamic of 'everyone can code' reminds me of the Unbundling -> Bundling -> Unbundling cycle of how entertainment media went from 3 broadcast channels to cable channels to web videos to our current state of bundled streaming services, with no end in sight.
Not to forget DVD shipping eras (Sony should not discontinue physical game sales, someone needs to get us silicon/holographic/terabyte recording media already), the focus is on the bundling/unbundling of software making. Slack + Salesforce vs 'we vibed our own chat and CRM just for us'...aka a Neo-SAP ERP program.
It's a cambrian explosion of software where lots are going to learn in realtime that the code was never the value of a software engineer / software, the value is in the shared understanding of an information system or workflow or business value stream, that parts of which happen to run through digital information systems.
By the time AI is done with us, its market effects mean in order to stay employable/added value beyond just widget-watchers, we will all be business strategy experts, or we will be algorithm-fed-doped-up mindless beasts, Morlocks vs Eloi, not because of AI itself, but because of the current rapaciousness of business leaders and an assumed trajectory where society doesn't adapt to social movements and actions now made possible by the existence of LLMs.
Sidneote: the LLMs themselves are not IMO going to achieve AGI any time soon. There might be a compilation of disparate systems and LLMs form part of 'tissues' of an eventual AGI, but not currently.
We will either be experts in asbestos chemistry, safety mechanisms, and remediation... or we'll be helping people tweak it for sale as kitchen sponges and pinatas.
Definitely. I wonder if this will be an increasing factor when choosing vendors. Will the security risk drive companies away from small software, due to their use of AI? Will we see a bundling of features in big established vendors?
Will they invest more into in-house security? (Doubting the last one).
For small apps used to deliver a service in my examples, I believe the risk surface is way narrower, which might make it more acceptable. For better of for worse
I like this and felt like some of it was much more fluid; but was I alone in feeling like the interjected "uh-huh" or "yeah?" moments felt a little jarring?
Almost felt a bit *uncanny valley* for what "natural" conversation is supposed to be like. If the "uh huh" isn't timed correctly, it'll feel like a zoom call with lag.
Every second of the interaction is uncomfortable to me, but I also have extreme difficulty with video calls with humans. The latency completely breaks my mind.
Brute forcing positive outcomes by spending more tokens until a happy path manifests does not solve the underlying comprehension (and liability) problem.
I fear a world where critical software is stood up with
increasingly non-human governed abstraction because it [seems like it] works.
Software engineers as the review terminal in a conveyor of business-led code mass production... coming to a company near you?
You're right, but you'd be lucky if a real human actually reviews any code. At my company, merging a PR still requires 2 humans to press "Approve" but I've been instructed that I don't need to read the PR, I only need to click "Approve". This is what 30 years of SWE experience is being used for now.
It feels like the gap is closing from an intelligence perspective. Or at least doing some kind of log flattening.
Been playing with GLM 5.2 in different contexts. It's less good if you don't max out thinking, but as xhigh it's been able to solve most problems I was throwing at Opus in the about the same amount of time (via OpenRouter).
Yesterday I compared Deepseek, Kimi 2.6, MiMo 2.5 and GLM 5.2 for the same task (replace a custom token-based auth scheme with a cookies-based scheme across a front- and back-end codebase).
I used Opencode with the zen subscription to try different models.
All did this perfectly, basically indistinguishable from each other. However, when I pointed out that the new cookies-based auth didn’t allow multiple independent logins across browser tabs (which the previous scheme did allow) I noticed this:
Deepseek, Kimi, MiMo started giving me multiple options but advocating strongly that I should either accept this deficiency, or don’t use the cookies version (keep the old auth scheme). They were so similar it was as if they were all the same model.
Only GLM 5.2 said “here’s how to use cookies and also have tab-level separation”. The difference vs the other models was very stark.
wondering how accurate the extrapolated distances are? Like the Rhino and the tree -- I wonder if the approximation could be useful in something like forensics from a security video?
> I wonder if the approximation could be useful in something like forensics from a security video?
No. It's just some plausible thing the AI made up. It's literally like asking an LLM to generate evidence of a crime based on the prompt you presented.
There are plenty of cool use cases, just not forensics.
Used properly I could imagine legitimate forensics uses to 3d reconstruction and splatting. For example as a "map" to find what underlying data (e.g. video frames) captured the thing.
