Flight: #SYG186P
ICAO code: #408298
Callsign: #SYNERGY
Operator: Synergy Aviation
Country: 🇬🇧
Speed: 788 kmh
Altitude: 12192 m
Distance: 7.6 km
Angle ∆: 58.2°
Direction ->: WNW
Track:
https://tinyurl.com/2aejf3fb
History:
https://www.radarbox.com/data/mode-s/408298
Seen: 2x
ADS-B Exchange - track aircraft live

ADS-B Exchange - track aircraft live - aircraft flight history

Love when a post feels like it was ghostwritten by a team of LinkedIn buzzwords on deadline. Who is the CEO? Which company? What culture? Why is AI involved? The mystery is the strategy. #AI #Synergy #CorporateVibes

Following up a sci-fi movie classical soundtrack with a sci-fi movie electronic score: The Jupiter Menace by Synergy (Larry Fast) on Passport records #vinyl #electronicmusic #soundtrack #movie #lp #synergy #analog #synths

I"m a sucker for cinematic analog synth brass and strings...

Stock #Ubuntu 24.04, #Wayland + #GNOME, I get this every time Synergy starts. It's been broken as well even once I allow permissions, but I haven't had the time/energy to look into what's wrong with it. Does anyone know if there is a no-fuss setup guide for Synergy when both client/server are on Wayland but not the same linux distro? Or should I be using some other app other than #Synergy for keyboard/mouse sharing now? I don't have a ton of spare time to "get linuxed" about this, I just need my shit to work because I have things to do.

P2: #dailyreport #conferences #synergy #learning #llm #agents
flexible biological programs may be.

In the self-driving car 🚗 speech, I noted an interesting
fact: agents have “prediction” to imagine the near
future before the reasoning step.

RL learns by trial and error with rewards, Imitational
Learning learns by copying experts, often used to "warm
start" RL, but sometimes combined for better
performance. This is simular to how LLM trained first by
self-supervised learning to predict next token and then
by RLHF.

Interesting DevEx practice 👨‍💻 shifts focus from
measuring development output to how the developer feels:
- Cognitive load – lower is better
- Flow state – higher is better
- Feedback loops – earlier is better

One speaker sees the AI future in: 🚀
- Edge computing
- AI assistants-coworkers
- Evolutionary, adaptive applications

P1: #dailyreport #conferences #synergy #learning #llm #agents
#reasoning
I was at the biggest party-conference for IT in Moscow. 🤖

The AIRI Institute head mentioned a very interesting
idea: that LLM reasoning is a form of task
decomposition. It explains why LLMs require additional
enforcement.
I think reasoning is not one process but three:
1) Chaotic search in memory
2) Decomposition heuristic that includes 1. as a substep
3) Logic-checking heuristic

AIRI suggests ways to increase AI agent robustness:
1) Full monitoring (with recording current state in plan)
2) Replanning: add rules to avoid doing something bad,
fallback strategies

The most challenging topic is to make the AI agent
improve its own code.

There was a demonstration of how biological embryonic
cells ꙮ act as small soldiers able to adapt, and how

Trying out #AnduinOS (#Ubuntu based). Liking it so far. Still think I'll change everything to #CachyOS when there's a #Synergy Flatpak. Set in my ways there.
Nothing says 'organic thought leadership' quite like 49 speakers in 7 days, one 200-character hashtag, and a URL that looks like your cat walked across the keyboard. 🚀📢 #synergy
×

P2: #dailyreport #conferences #synergy #learning #llm #agents
flexible biological programs may be.

In the self-driving car 🚗 speech, I noted an interesting
fact: agents have “prediction” to imagine the near
future before the reasoning step.

RL learns by trial and error with rewards, Imitational
Learning learns by copying experts, often used to "warm
start" RL, but sometimes combined for better
performance. This is simular to how LLM trained first by
self-supervised learning to predict next token and then
by RLHF.

Interesting DevEx practice 👨‍💻 shifts focus from
measuring development output to how the developer feels:
- Cognitive load – lower is better
- Flow state – higher is better
- Feedback loops – earlier is better

One speaker sees the AI future in: 🚀
- Edge computing
- AI assistants-coworkers
- Evolutionary, adaptive applications