If this is just another librechat I'll be disappointed, and frustrated- because this is so 5 months ago.

On the 'extreme' bit, this might certainly be along the lines of personal experience- but I find LinkedIn, HN, and Reddit to be among the worst offenders in the basket of attention hogs / dopamine sinks.

My reasoning there is mainly rooted in the fact that these platforms all present a couple dozen or so items that are one click away from soaking your brain (whether that be comment sections, or the parent item itself.)

Social media being work related doesn't necessarily mean that the platform lies on either end of the spectrum here, to me. I work on AI things, and work related social media is certainly an avenue thats detrimental to me- in the fact that the aforementioned platforms produce more content that I can reasonably say I have the ability to consume in a healthy manner.

Again, your experience may be different; but I wouldnt put those platforms anywhere near what cold turkey might mean for me. For me, still consuming relevant work stuff while dialing down away from the extreme would probably be sticking to one or two newsletters, which post ~monthly or so.

I think if the platform is designed in such a way that the goal of the average post is aligned with whats detrimental to attention/focus/dopamine-response, then it can't possibly be used in a straightforwardly healthy manner.

Ie instead of

"hey agent, use the api to get X, then do unpredictable mutation Y to it, and email it to me",

it should be;

"Scheduled task runs code to get X then place it in workspace. Task then starts agent in workspace and is prompted to do mutation Y to it. Post-flight code then sends final product."

The difference here being that the pre/post flight stuff is called deterministically, rather than called by an llm.

Though, imo, the fact that pi maintains its "we only include the bare minimum!" statement is part of the draw for me. Especially considering that im in an enterprise env; being able to internally share custom implementations of out-of-the-box Codex/CC stuff is really nice.

I do wonder how they'd go about shipping a default web search tool. Big problem there is the lethal trifecta. Shipping something that arbitrarily allows untrusted content to be retrieved non-deterministically I'm sure is a long conversation on Pi's end. Pushing it off to the user to decide is easy.

To make an anology, in my mind, this is akin to saying "fuel air mixture system" when referring to direct fuel injection specifically, when of course, a carburetor also lives in that category.

Reading your comment reminded me; I actually did something quite similar at $MY_BETTER_STARTUP! My approach is slightly different, however, employing what I like to call State-Horizon-Aware-Rercursive-Threaded-Graph-Position-Topology.

With a 400% increase in words, $MY_WAY_BETTER_HARNESS looks to be about four times as performant. SHARTGPT isn’t just a harness engineer's playground — it's a the cyber jungle gym that frees them from $MY_HARNESS.

If you want, I can even include a sentence or two that will really tell those potential investors why they should shower you with money instead of the other commenter! Just say the word!

(Looking for lead investors for our angel syndicate btw! DM me if interested)

In any case. There are solutions in the comments on the issue, as well as this hn thread.

From watching Pr's and issues- seems like openai at least wants to come across as if theyre supporting non-oai models :/

As far as I can tell, they tick the same boxes- but one has the support of a big boy model provider.

I'll take the opportunity to note that if you're running solid evals, you'll have data to back the efficacy of your system. If you are seeing a hallucination rate of 1%, then you certainly should be working on your harness/toolset/context/prompting etc.

Saying "1% hallucination rate..." is akin to saying "30,000mi lifespan for [modern japanese make engine]". Something is wrong.

The data you need to get into context for a small model, vs a big boy frontier model, vs a fine tuned open weight big boy- are all very different. I can understand what they're doing here, and most of the 'why', but- not all of the why.

Observability is, for my purposes, solved by a given framework supporting OpenTelemetry.

Guardrails is where I've gotten the most value of openshell being a neat package. Agent workload scope is written as policy in openshell, and capability is backed by openshell handling all execution.

Monitoring/deployment/versioning is helped as well, depending on how agents/runners are slotted into the system. Deployment namely is quite well supported- openshell has kube/helm bits that are experimental atm, but seem like a logical approach imho.

Evals and a/b testing isnt something ive explored in depth, considering that agents with composable tool sets + frontier models are beyond my expectations already.