Aydın Tiryaki and Google Gemini

In the world of artificial intelligence, the May 19 updates were announced as a great revolution. With these updates, massive “context windows” were introduced, and on paper, it was aimed for systems to take in and process thousands of pages of data at a single time. However, the theoretical capacity reaching such massive dimensions did not always improve practical focus in a dynamic conversation. On the contrary, it gave birth to a brand new paradox where artificial intelligence loses context in long conversations much more easily than before.

The Focus Loss Paradox: Memorizing the Library and Forgetting the Book

Systems that were able to catch the context better in long conversations with much narrower memories in the past began to lose that focus when they attained massive memories. Technically, this situation can be explained as the “dilution of the attention mechanism”.

Having a very large memory space created a side effect that made it difficult to follow the thin, interconnected conversational threads within that memory. The system almost memorizes an entire library but loses the emotion and context of the specific question of that moment within that massive pile of data. As a result, while having it write a long-running article series, after a while, the system either confessed to losing the context or completely “messed up” and started producing irrelevant results (spouting nonsense). This situation made it impossible to directly evaluate prolonged conversations within the same window.

To overcome these “context disconnections” and write qualified, long article series, developing next-generation strategies and “hybrid” solutions instead of relying on a single model became inevitable.

The Static Anchor: NotebookLM’s Flawless Architecture

The first analytical solution developed to overcome these crises was to take the transcripts of the lengthening and branching conversations and upload them to NotebookLM.

Instead of using a dynamic conversational memory that flows freely and tends to scatter with probabilities, NotebookLM works with a static structure tightly anchored directly to the documents you upload. In dynamic conversations, AI tends to arbitrarily summarize the rich data at hand on its own initiative and, so to speak, “butcher” it. Whereas NotebookLM processes the documents without causing context loss issues, staying true to that flawless meticulousness and data integrity demanded in article writing standards.

For Long-Running Series and Heavy Lifting: The Claude Reality

The second and much more strategic option is to turn to Claude, which has much higher context fidelity when working with massive data sets.

Claude’s architecture offers a structure specifically optimized for keeping information active for a long time and “focusing” within wide context windows. It remembers the massive data loaded into the system or those strict writing rules determined (for example, “never shorten previous texts, only add the new information”) to the letter, even in the final article of the series. Its uncompromising obedience to structural text construction and detailed system instructions has made it the number one choice for long-running projects and serial article productions.

The Right Tool for the Right Job: The Hybrid Working Model

No model is flawless in every task; each has its own unique muscle structure. This new strategy, which bypasses the handicaps in long contexts with NotebookLM and Claude, is actually a flawless production line logic.

In this hybrid setup, Gemini is now positioned not so much for the construction of the final article, but as a “first ignition laboratory” where raw ideas are clashed, wordplays (like tertip, tefriş, tanzim) are flexibly tested, and the limits of probabilities are pushed to the very end. The skeleton is built here, the philosophical brainstorming is done here. However, when it comes to that “heavy lifting” and long text construction where no detail should be missed, the task is handed over to Claude, which stands out with its context fidelity, or to NotebookLM with its static anchor.

This approach is one of the most visionary ways to manage artificial intelligence like a conductor by combining their strengths, rather than surrendering to the vulnerabilities of the systems.

| aydintiryaki.org | YouTube | Aydın Tiryaki’nin Yazıları ve Videoları │Articles and Videos by Aydın Tiryaki | Bilgi Merkezi│Knowledge Hub  | ░ Virgülüne Dokunmadan │ Verbatim ░ | ░ Bir Kelimenin Peşinden Yapay Zekanın Derinliklerine: “Tebdil-i Mekan”dan “Zırvalama”ya Uzanan Bir Söyleşinin Anatomisi │From a Single Word to the Depths of AI:The Anatomy of a Dialogue Spanning from “Tebdil-i Mekan” to “Spouting Nonsense” ░ 02.07.2026

Credits: The subject, scope, and editorial framework of this article series were determined by Aydın Tiryaki. Gemini (Google, Advanced / Pro mode) assisted during the initial 35-stage interactive dialogue that evolved from the concept of “tebdili mekan”; while NotebookLM assisted in analyzing this dynamic conversation, expanding it into comprehensive articles, and executing the bilingual writing and translation process.