Context Engineering in 2026 — Louis-François Bouchard, Omar Solano & Samridhi Vaid, Towards AI
The cheapest configuration they tested was the one sending the most tokens. Across 11 presets run against their open source AI tutor, doing nothing at all to the context beat every compaction technique on recall, cost, and latency at once, and their own production defaults scored worse than leaving the history alone. Prompt caching is why. With 97% of tokens served from cache, and cached tokens up to 50 times cheaper on some APIs, compaction has to shrink a context by more than 50 times before it pays for itself, because rewriting the context invalidates the cache. Louis-François Bouchard's framing is that summarization is potentially a trap.Omar Solano walks through the architecture and the first run, including a knowledge base browsing tool they built, measured, and found returned identical recall while running 50% slower. Samridhi Vaid extends it: keeping the full history recovered specific details 95% of the time against 32% after summarizing, and distinctive facts survived to 800,000 tokens without visible rot. Hardware changes the answer, though. Capped locally at a 32k window, keeping everything stops being possible, and a larger parameter count does not buy a larger context window. Dense retrieval fell to 0% recall on facts buried at 400k tokens where BM25 still found them every time. The rule they land on is to name the constraint you actually have before reaching for compaction, rather than compacting by default.
Speaker info:
Louis-François Bouchard (Towards AI):
- https://x.com/Whats_AI
- https://www.linkedin.com/in/whats-ai/
- https://www.louisbouchard.ai
Omar Solano (Towards AI):
- https://x.com/omar_solano1
- https://www.linkedin.com/in/omar-solano1
Samridhi Vaid (Towards AI):
- https://x.com/samridhivaid
- https://www.linkedin.com/in/samridhivaid/
Project:
- https://github.com/towardsai/ai-tutor-app
Timestamps:
0:00 - The problem is the context, not the model
1:30 - The AI tutor, and its five requirements
5:21 - Two root problems: a finite window, a stateless model
7:52 - Context rot, cost, and latency
9:13 - The compaction toolkit, with and without an LLM
12:58 - Offloading to files, the LLM wiki, progressive disclosure
16:47 - Prompt caching, and why compaction can backfire
19:20 - When to clear, compact, and optimize for cache hits
21:53 - The tutor's architecture, a single ReAct agent
25:49 - Hybrid search over an 8 million token corpus
28:28 - Letting the agent browse the knowledge base
31:01 - The browse tool measured: same recall, 50% slower
36:23 - The experiment setup: presets, tasks, harness
42:55 - Results: doing nothing wins on all three fronts
48:13 - Should you ever compact?
49:32 - DeepSeek, and a 50 times cache discount
50:53 - Memory: 95% against 32% after summarizing
54:38 - Cost at scale, and going local
57:09 - Local limits: bigger models, same window
58:25 - Where dense retrieval fails and BM25 holds
1:01:01 - What they finally chose Receive SMS online on sms24.me
TubeReader video aggregator is a website that collects and organizes online videos from the YouTube source. Video aggregation is done for different purposes, and TubeReader take different approaches to achieve their purpose.
Our try to collect videos of high quality or interest for visitors to view; the collection may be made by editors or may be based on community votes.
Another method is to base the collection on those videos most viewed, either at the aggregator site or at various popular video hosting sites.
TubeReader site exists to allow users to collect their own sets of videos, for personal use as well as for browsing and viewing by others; TubeReader can develop online communities around video sharing.
Our site allow users to create a personalized video playlist, for personal use as well as for browsing and viewing by others.
@YouTubeReaderBot allows you to subscribe to Youtube channels.
By using @YouTubeReaderBot Bot you agree with YouTube Terms of Service.
Use the @YouTubeReaderBot telegram bot to be the first to be notified when new videos are released on your favorite channels.
Look for new videos or channels and share them with your friends.
You can start using our bot from this video, subscribe now to Context Engineering in 2026 — Louis-François Bouchard, Omar Solano & Samridhi Vaid, Towards AI
What is YouTube?
YouTube is a free video sharing website that makes it easy to watch online videos. You can even create and upload your own videos to share with others. Originally created in 2005, YouTube is now one of the most popular sites on the Web, with visitors watching around 6 billion hours of video every month.
