Tyler Hutcherson

2.1K posts

Tyler Hutcherson banner
Tyler Hutcherson

Tyler Hutcherson

@tchutch94

AI Engineering @Redisinc | trying to figure out what’s up | UVA alum 2x

Richmond, VA Katılım Kasım 2012
789 Takip Edilen473 Takipçiler
Tyler Hutcherson retweetledi
Ava
Ava@noampomsky·
friend is in the stage of claude psychosis where he asks claude to send him newspapers about what claude is doing for him
Ava tweet media
English
258
450
8.6K
400.5K
Tyler Hutcherson
Tyler Hutcherson@tchutch94·
@mjasay This has for sure been my experience (and largely for the team). Feeling the pressure of semi-traditional SDLC expectations along with increased firehose of requests/needs/issues on OSS materials and then general AI fomo. Will keep you working longer than normal 😅
English
0
0
0
24
Matt Asay
Matt Asay@mjasay·
Tl;dr we work for the robots now
Matt Asay tweet media
Nav Toor@heynavtoor

🚨BREAKING: Berkeley researchers spent 8 months inside a tech company watching how employees actually use AI. The promise was simple: AI will save you time. Do less. Work smarter. The opposite happened. Workers didn't use AI to finish early and go home. They used it to take on more. More tasks. More projects. More hours. Nobody asked them to. They did it to themselves. The researchers sat inside the company two days a week for 8 months. They watched 200 employees in real time. They tracked work channels. They conducted 40+ interviews across engineering, product, design, and operations. Here's what they found. AI made everything feel faster, so people filled every gap. They sent prompts during lunch. Before meetings. Late at night. The natural stopping points in the workday disappeared. People ran multiple AI agents in the background while writing code, drafting documents, and sitting in meetings simultaneously. It felt like momentum. It felt productive. But when they stepped back, they described feeling stretched, busier, and completely unable to disconnect. 83% said AI increased their workload. Not decreased. Increased. 62% of associates and 61% of entry-level workers reported burnout. Only 38% of executives felt the same strain. The people doing the actual work absorbed the damage while leadership celebrated the productivity numbers. Then came the trap nobody saw coming. When one person uses AI to take on extra work, everyone else feels like they're falling behind. So the whole team speeds up. Nobody formally raises expectations. But the new pace quietly becomes the default. What AI made possible became what was expected. The researchers gave it a name: workload creep. It looks like productivity at first. Then it becomes the new baseline. Then it becomes burnout. AI was supposed to give you your time back. Instead it's eating more of it. And the worst part? You're doing it to yourself. Voluntarily.

English
2
1
5
1.1K
Tyler Hutcherson retweetledi
Sam Youngman
Sam Youngman@samyoungman·
Stephen Miller: he was a domestic terrorist The reality: he was an ICU nurse who was trying to help a woman
English
8
154
1.9K
16.6K
Tyler Hutcherson
Tyler Hutcherson@tchutch94·
One of the joys over the last few months has been collaborating on this new @DeepLearningAI course on semantic caching in agentic AI systems. Huge thanks to the whole @Redisinc team (@ilzhechev) and @AndrewYNg for the partnership and support! 🙏🏼
Andrew Ng@AndrewYNg

New course announcement: Semantic Caching for AI Agents, taught by @tchutch94 and @ilzhechev from @Redisinc. Semantic caching can significantly reduce your AI application's inference costs and latency. If someone asks "How do I get a refund?" and another later asks "I want my money back," semantic caching recognizes these mean the same thing so it can use a cached response instead of making another model call. This short course takes you from building your first semantic cache from scratch to implementing production-ready systems using Redis' open-source tools. Skills you'll gain: - Build semantic caches from scratch, then implement them using Redis' SDK with production features - Measure cache performance using hit rate, precision, recall, and latency - Enhance accuracy with threshold tuning, cross-encoders, LLM validation, and fuzzy matching Join and learn to reduce your agentic AI's costs and improve speed! deeplearning.ai/short-courses/…

English
2
7
17
6K
Tyler Hutcherson
Tyler Hutcherson@tchutch94·
It’s a hungry bunch. The landscape moves fast and is competitive. But if you’re looking for something customer/partner facing and rewarding… hit me up. 🤙🏼
English
0
0
0
82
Tyler Hutcherson
Tyler Hutcherson@tchutch94·
Applied AI Engineering at Redis goes way back to when me @SamPartee and @taimurrashid began the team to incubate the AI business for Redis. Many years later, we’ve grown in impact and visibility even more! Think of it as a “forward deployed engineering team”
English
1
0
1
113
Tyler Hutcherson
Tyler Hutcherson@tchutch94·
Still hiring for a high impact applied AI engineer at @Redisinc in the Bay Area. Looking for a role with high agency and influence? shoot me a DM: - you understand how to build and scale agentic systems - you obsess over customer pain - you seek and build repeatable solutions
English
3
0
5
216
Tyler Hutcherson retweetledi
Santiago
Santiago@svpino·
If you can't understand how good your product is, you don't have a product in the first place. Everyone needs evals (even if they don't understand what "evals" actually mean). The number one problem I've had to fix when working with teams that can't make progress because their product is running in circles: 1. Find out the metric we care about the most 2. Automate the evaluation of the product 3. Error analysis to focus on the most critical issues and fix them. I really liked @sh_reya's post, and I love the framing of the question teams should ask: "When can we afford to be less rigorous, and when can we not?"
Shreya Shankar@sh_reya

i did not expect to wake up this morning and write a blog post

English
4
8
45
18.1K
Tyler Hutcherson
Tyler Hutcherson@tchutch94·
@NirDiamantAI This is big time. Huge lift to get off the ground with real world agent workflow. This repo gets you started in the right direction 💯
English
1
0
1
47
NirD
NirD@NirDiamantAI·
Incredible milestone! My Agents-Towards-Production GitHub repository just crossed 10,000 stars in only two months! 🌟 github.com/NirDiamant/age… Here's what's inside: ✅ 33 detailed tutorials on building the components needed for production-level agents. ✅ Tutorials organized by category. ✅ Clear, high-quality explanations with diagrams and step-by-step code implementations. ✅ New tutorials are added regularly. ✅ I'll keep sharing updates about these tutorials here. A huge thank you to all contributors who made this possible! ♻️ Repost to share with others
English
2
6
36
2.8K
Tyler Hutcherson retweetledi
Redis
Redis@Redisinc·
🎉10K⭐️🎉 Go check out the repo for tutorials, components, and more to help you build agents.
NirD@NirDiamantAI

Incredible milestone! My Agents-Towards-Production GitHub repository just crossed 10,000 stars in only two months! 🌟 github.com/NirDiamant/age… Here's what's inside: ✅ 33 detailed tutorials on building the components needed for production-level agents. ✅ Tutorials organized by category. ✅ Clear, high-quality explanations with diagrams and step-by-step code implementations. ✅ New tutorials are added regularly. ✅ I'll keep sharing updates about these tutorials here. A huge thank you to all contributors who made this possible! ♻️ Repost to share with others

English
1
1
2
782
Tyler Hutcherson retweetledi
LangChain
LangChain@LangChain·
Join @Redisinc and @langchain on July 23 for a webinar to learn how LangGraph + Redis make it easy to build AI agents with real memory. In the live webinar, you'll get demos, performance tips, and hear from the engineers behind the integration. 👉 RSVP here: events.redis.io/breakingdown-l…
LangChain tweet media
English
8
32
176
15.3K
Tyler Hutcherson retweetledi
Avi Chawla
Avi Chawla@_avichawla·
The fastest serving engine for LLMs is here (open-source)! LMCache is an LLM serving engine designed to reduce time-to-first-token and increase throughput, especially under long-context scenarios. It boosts vLLM with 7x faster access to 100x more KV caches. 100% open-source!
Avi Chawla tweet media
English
13
140
717
54.4K
Tyler Hutcherson
Tyler Hutcherson@tchutch94·
@TheJeffersonC Genuine question — all of the timelines, posts, and complaints on here consistently refer to hand wavy “discriminatory programs and policies”. It’s vague. Point me (us) to the exact list of those measures that were illegal and their impact.
English
1
0
5
508
The Jefferson Council
The Jefferson Council@TheJeffersonC·
For those just catching up or who haven’t been paying attention, here’s the timeline that led to Jim Ryan’s resignation. This is a university president who repeatedly asked the DOJ for deadline extensions, failed to provide a shred of evidence that UVA was complying with federal anti-discrimination laws or Supreme Court rulings, and gave the public zero transparency about dismantling illegal DEI programs. All the while, he was telling the Faculty Senate that nothing would change regarding DEI. He was openly discussing suing the Trump administration and was the only public university president in Virginia to sign the AAC&U letter. This wasn’t leadership. It was calculated defiance that finally caught up with him.
The Jefferson Council tweet media
English
44
153
581
104.4K
Tyler Hutcherson retweetledi
Taranjeet
Taranjeet@taranjeetio·
Introducing the OpenMemory Chrome Extension! 🎉 Shared memory you can carry across ChatGPT, Claude, Perplexity, Grok, Gemini & more. Think universal context, synced across every AI assistant you use. It's free and opensource. Try it Now!
English
212
120
1K
184.6K
Tyler Hutcherson retweetledi
NirD
NirD@NirDiamantAI·
🔥 A FREE goldmine of tutorials for building production-ready AI agents! 🔥 Just launched a GitHub repo packed with 25 hands-on tutorials that walk you through every core component of an AI agent pipeline. 🔗 Check it out here: github.com/NirDiamant/age… The tutorials are grouped into: 1. Orchestration 2. Tool integration 3. Observability 4. Deployment 5. Memory 6. UI & Frontend 7. Agent Frameworks 8. Model Customization 9. Multi-agent Coordination 10. Security 11. Evaluation Found it helpful? Drop a ⭐ on the repo!
English
34
71
368
88.3K
Daniel Svonava
Daniel Svonava@svonava·
I've been answering questions on our github lately, finding out about companies running superlinked OSS in prod and get this today... onwards and upwards :-)
Daniel Svonava tweet media
English
1
0
6
487
Tyler Hutcherson retweetledi
hammad 🔍
hammad 🔍@HammadTime·
ok. enough is enough. RAG vs agentic retrieval. you 👏 are 👏doing 👏 retrieval. Call grep? Retrieval. Do a vector search? Retrieval. Use BM25? Retrieval. This dichotomy is just marketing by people with an incentive to make you use their product, for fear of not keeping up with the latest thing. These are all valid techniques to search data, and give it to a model. The marketing is a distraction. Reason about and measure what works for your application. Decide what you do based on that.
English
16
13
175
17.9K