Gemini 1.0 Pro Deprecated vs Gemini 2.5 Flash Image

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I’ve been seeing lots of posts that discuss the subject of AI use as a way to connect with your F/O. The negative impact of AI has already been highlighted plenty of times in this sub, so I will not waste time repeating what’s already been said.

Instead, I will share my own experience with AI chat bots and try to explain why I found it lacking in comparison to other alternatives after a prolonged use. With this post I am hoping to reach either those who are tempted to try AI for roleplay or those who already do.

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I’ll preface this by saying I used to be obsessed with chatting with AI chat bots. Find a new character I like? Janitor, here I come. New sona idea? Hell yes, plenty of bots to try it out with. And there were some very creative bots I saw people make too, which made me excited to write myself into their story. That lasted for around two years, coming in waves where I would be on my phone chatting 24/7 for around a week or so before getting burnt out and leaving for a few months before the cycle would begin all over again.

Now, I have reached a point where I can’t even look at AI-writing without feeling sick. Here are the reasons why that go beyond the common arguments you hear against AI:

>**| It stops feeling real. |**

No matter which model you use, if you have talked to a bot for long enough you will inevitably start seeing patterns in the way it talks, the way it describes things, the decisions it makes.

And I don’t mean just certain phrases like “you belong to me, mind body and soul” or “you’re playing with fire”. Oh no, what I mean is that every single sentence will have that artificial feel to it that you can’t explain. You may try to give it directions: to use less metaphors, tell it to sound more human. And it may make it bearable for a while. But once your brain has seen enough of it, nothing will ever help you get rid of that feeling of “something is off but I don’t know what”.

**Note:** To back up what I’m saying, here are some of the models I’ve tried and faced the same problems: Claude Opus 4.1, Claude Sonnet 4.5, Gemini Pro 2.5, DeepSeek R1T Chimera, DeepSeek R1, GLM 4.6, among others.

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>**| It doesn’t actually make sense. |**

This one is hard to see through at first. When you start out role playing with AI, it feels novel and magical. The plot seems to write itself and you truly feel like the potential is limitless. Sometimes, the AI even surprises you.

However, after a while you start realizing that the AI doesn’t actually think. You may think that this is obvious, but when you are fully engaged in the story it does not cross your mind that the AI makes things up as it goes for every individual reply, because from your human perspective the story makes sense. The AI does not have an understanding of set up and payoff. It is incapable of writing a good story and it does not understand how human relationships work.

If you’re still unconvinced, here’s the thing that really made me pause when I first realized it after hours of rerolls and frustrated OOC instructions — it does not actually have a consistent personality.

What do I mean by that? Let’s say your F/O is a confident, charming, flirtatious and extroverted dreamer who is a huge romantic, with his main flaw being that he is selfish when it comes to the things he wants. The AI will try to follow that description and act out that personality, using the language that is usually associated with such characters in media.

At first, it will seem to be successful, especially if the bot is very well-written. The problems will arise when it starts needing to make decisions that matter. Because that is when your beloved character will turn into a caricature of themselves with zero nuance or depth until it gets to a point where you need to spoon feed the AI for it to give you what you want.

Suddenly, the selfish dreamer who is faced with the possibility of their lover breaking up with them needs to decide how to act — do they grovel? do they use force? do they bargain? Their reaction will depend on countless factors that the AI simply will not take into account, because it doesn’t have an understanding of what “realistic” is. It will read the trait “selfish” and insert a reaction that it thinks will fit in accordance to what it’s seen before.

Perhaps it will make the character blame the user, because that is what selfish people do. However, that will be entirely out of character, because they are also a huge romantic and do not want to lose their lover. But that’s okay, we can reroll. Now they’re crying and apologizing, but wait, that character is also meant to be confident and self-assured, would their pride really allow them to go about it this way? See what I’m getting at here? There is no thought being put into this. It’s guess work through and through. And the AI goes through this exact process every single time it tries to think of a response for you.

Isn’t that so boring? You’re not even speaking to an approximate version of your F/O. You are speaking to a collection of traits that the AI thinks make your F/O who they are while masquerading as them and changing their tune every other reply. And God help you if your F/O has an actually complex and layered personality.

Also, you may argue that some models have a feature that forces them to “think”. I know what it is, I’ve used it and seen how it works, and I can confidently say that there is no actual meaningful thought happening there. It is still very narrow focused on tackling one task — responding to your LATEST message. It does not work as a means to make the story have foresight.

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>**| You can do better. |**

After chatting with AI for long enough, at a certain point you find yourself giving corrections to it more than actually role playing, and that is when it’s a good idea to take a step back and think if what you’re doing is actually worth it.

When the character has been mischaracterized, reduced to a stereotype, or simply made to sound artificial, even if technically still in character, it really ends up being easier to just use your head — fantasize, write your own stuff. Not because you’re purposefully trying to restrict yourself (if you’re addicted, this won’t work), but because it’s just… better.

I was honestly shocked by how much more in character I could make myself sound in comparison to anything the AI chat bots could give me. I will warn you though: if you have already been resorting to talking to AI for a while now, it will be hard at first. It was for me, at least. Because I have characters I used to role play with that are the complete opposite of me, and I was tempted to ask AI how they’d react in certain situations even as I tried to write it myself. And sometimes I did. And guess what? I ended up using none of the phrases it gave me and thought of my own that sounded 100 times better every single time.

Because your brain works well when it’s cornered. I was so profoundly disappointed with anything that AI could offer me that I simply had no other choice but to think of my own solutions just to satisfy that role playing itch, and that is when I learned that from the start AI was never going to be it for me or anyone who knows what quality writing is like.

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**TL;DR:** AI role playing is a pointless endeavour because after you have chatted with it long enough you will understand that it does not think or actually understand your F/O’s character, as well as incapable of writing a consistent story that makes sense. If you rely on it as the sole means of connecting with your F/O you will only be set up for disappointment when the bot will inevitably stop feeling and sounding real. You are better off using your imagination or writing the same stories yourself.

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Playing around with Gemini 2.5 Flash Image (sorry, not calling it that other name) just now, I felt like Oppenheimer staring at the fireball. Such an enormity of new power, so suddenly.

The masturbators of tomorrow will marvel that people were once limited to non-customized pornography.

Seriously, I think this changes everything.

Stop blaming Claude. Your harness is the problem.

I've been running Claude Code on Opus 4.7 for 8+ hours a day on Max 5x. Zero quota issues. Here's what I actually did.

Most people complaining about Claude "going dumb" or "eating tokens" set it up like this: no memory, no tools, no rules, dump 40 files into one context window, then wonder why it hallucinates. That's not a Claude problem.

Context discipline cuts token usage roughly in half

Put a CLAUDE.md at your repo root. Stack overview, ownership matrix, hard rules — run tsc --noEmit after every edit, max 50 lines per bugfix, one fix per commit, never touch auth/Stripe/middleware without explicit approval. It loads every session. Claude stops asking the same questions.

Persistent memory lives at ~/.claude/projects/yourproject/memory/ — typed markdown files with prefixes like user_, feedback_, project_, reference_. Keep an index in MEMORY.md. You stop re-explaining your project at the start of every conversation.

Biggest single quota win: subagents for grep-work. Spawn an Explore or general-purpose agent to do the file-digging. They burn their own context, return a summary. Your main window stays clean.

Workflow discipline is where most setups fall apart

Auto-retros after every non-trivial session. Save them to docs/retros/YYYY-MM-DD-topic.md. The next session loads the latest retro automatically — continuity without re-briefing.

verification-before-completion as a hard rule. Claude cannot say "done" or "fixed" without running the verify command and showing you the output. Kills hallucinated success completely.

Atomic commits, one fix per commit, hard line limits. Clean history, easy rollback, and it forces Claude to actually scope its work.

For architecture decisions or anything involving security/migrations: one phrase triggers Claude to spawn Gemini Pro + Flash + Sonnet in parallel and synthesize. Three independent reads are better than one confident monologue.

MCP servers — let it act instead of copy-pasting

The ones I actually use:

- supabase — SQL, migrations, schemas directly from chat
- github — PRs, diffs, issues, file reads
- chrome-devtools-mcp + playwright — Claude can browse your deployed site, take screenshots, evaluate JS. It QAs itself.
- context7 — current library docs, not stale training data. Kills a specific class of hallucination entirely.
- firecrawl — on-demand scraping
- sentry — production errors read and triaged from chat
- gemini MCP — powers the multi-model consultation panel

OSS worth actually installing

graphify — takes any input (code, docs, papers, images) and produces a clustered knowledge graph as HTML + JSON. On large repos, Claude reads the graph instead of 200 files. Massive.

claude-flow — swarm orchestration, hooks, memory coordination, SPARC, TDD, code review swarms. github.com/ruvnet/claude-flow

Superpowers skills — search "superpowers skills claude code" on GitHub. The ones I use most: systematic-debugging, verification-before-completion, dispatching-parallel-agents, test-driven-development.

CodeRabbit skill reviews diffs and auto-fixes review comments. Claude Retrospective skill generates the retros mentioned above.

Hooks automate the grunt work

PreToolUse, PostToolUse, SessionStart, PreCompact, Stop. Auto-save memory, auto-run tsc on edits, sync state before compaction. Claude thinks, the harness does the janitor work.

TL:DR!

1. Write CLAUDE.md
2. Turn on persistent memory
3. Install graphify + claude-flow + 6-7 MCPs
4. Auto-retros + verification-before-completion as non-negotiables
5. Subagents for grep and file exploration
6. 50-line limit per bugfix
7. Consultation panel for hard calls

5+ hours a day, ~250 tool calls per session, atomic commits, full deploy → screenshot → verify cycles. Max 5x, no quota hit.

Claude isn't the problem. The harness is!

EDIT: https://github.com/anothervibecoder-s/claudecode-harness

I made a claude.md example based on my CLAUDE.md file, you can tell claude to fill this based on your projects!

If it helped, just star it!

Fellow Regards and Degenerates,

I'm here to tell you that $GOOGL / $GOOG is the most criminally undervalued stock in mega-cap tech because it’s the undisputed leader in the technologies that define the next century. Forget the short-term noise. This is a deep dive into the strategic moat that others can't even dream of crossing.

**1. Future of Tech**

**Waymo**

Google's Waymo is WAY MORE than a competitor. It's the only fully scaled, commercialized Level 4 self-driving service available to the public. It operates 24/7 robotaxi services in multiple major US cities like Phoenix, San Francisco, Los Angeles, Austin and testing in other cities

In San Francisco, its massive surge in volume has already resulted in its market share surpassing Lyft's, making it the city's second-most popular ride-hailing service. It’s the result of a decade-plus of calm, deep-pocketed investment, allowing it to log over 100 million fully autonomous miles and complete over 10 million paid trips.

The sheer mileage, the complexity of the scaled deployments—which have demonstrated an 80% reduction in injury-causing crashes compared to human drivers—and the fact that they are now expanding internationally to places like Tokyo and London is a moat that no other company has even come close to building. The heck, there is no second competition in autonomous self-driving.

**Quantum Leap for Humanity**

The recent quantum discovery by Google, featuring its Quantum Echoes algorithm, is a major step toward making quantum computers a practical, powerful tool. This breakthrough, which demonstrated verifiable quantum advantage on the Willow quantum chip, is set to accelerate scientific discovery across key industries.

Specifically, the ability to perform verifiable quantum advantage means we can now trust a quantum computer to reliably solve real-world physics problems that are computationally infeasible for classical machines.

What Quantum Echoes Will Do

This breakthrough directly accelerates the original promise of quantum computing:

* Design Better Drugs and Cures: The Quantum Echoes algorithm ran 13,000 times faster on Willow than the best classical algorithm on one of the world's fastest supercomputers. This technique—which is already being used in a quantum-enhanced version of Nuclear Magnetic Resonance (NMR) to study molecular structure—will dramatically cut the time it takes to discover and develop new, more effective medicines by providing unprecedented insights into how potential drug compounds interact with disease targets.
* Create Advanced New Materials: The algorithm's power to reveal previously undetectable details about atomic interactions will unlock the discovery and design of novel materials. This is vital for creating the next generation of:
* High-Performance Batteries (for electric vehicles and energy storage).
* More Efficient Solar Cells.
* Lighter, Stronger Polymers for manufacturing and aerospace.

In short, Google's Quantum Echoes is an engineering milestone that moves quantum computing from a theoretical concept to a practical, verifiable machine for solving humanity's hardest scientific problems.

Think of it this way - The average age of a few generations from now will be approximately 100 years. This is truly remarkable.

**AI: The Medical Revolution**

AI, particularly from Google DeepMind, is already achieving breakthroughs that save time, money, and lives. This is AI's immediate, profitable impact.

* AlphaFold & Isomorphic Labs: AlphaFold, an AI model from DeepMind, solved the 50-year-old problem of protein folding. This monumental achievement earned Google DeepMind's Demis Hassabis and John Jumper a share of the 2024 Nobel Prize in Chemistry (along with David Baker). In simple terms, proteins are the body's tiny machines. Knowing their 3D shape is the blueprint for creating drugs. AlphaFold can find that blueprint in minutes, a process that used to take years. Isomorphic Labs is now using this and other advanced AI to design new small-molecule drugs from scratch at "digital speed," accelerating drug discovery from years to months.
* AI and Quantum Synergy: This is where the magic happens. AI (the brain) helps guide the ultra-powerful quantum computer (the brawn) by identifying which molecules to focus on and then analyzing the quantum simulation results. This hybrid approach makes breakthroughs possible that would be computationally impossible otherwise. Google is the only company with a dominant lead in *both* technologies.

**2. AI Supremacy: The Foundational Architect**

The current AI boom exists because of Google, and its competitive position is strong due to decades of strategic investment focused on making powerful technology affordable enough to scale effectively. By now, it is widely known that the foundational technology for modern AI—the Transformer architecture—was created by Google.

* Models: Leading Across the Modalities Google has established market-leading or top-tier models across text, image, and video.
* Text & Multimodal: The Gemini family of models sets the pace in multimodal reasoning, handling text, code, audio, and video inputs.
* Image (Nano Banana/Imagen): The technology powering Nano Banana (Gemini 2.5 Flash Image) excels at enterprise-critical tasks like advanced editing that preserves character/product consistency across iterations—a crucial capability for marketing and design.
* Video (Veo): Google's cutting-edge video generation models, like Veo, are rapidly advancing the state-of-the-art in creating high-quality, long-form video content.
* Infrastructure: The TPU Efficiency Moat Google designs its own custom AI chips, the Tensor Processing Units (TPUs), which are engineered for peak AI efficiency and low-cost operation. They have spent years perfecting this hardware because a tech needs to be affordable for it to scale and work. This commitment to efficiency is so superior that competitors, including major AI labs, must increasingly rely on the latest generations of Google's custom hardware by coming to Google Cloud Platform (GCP) to train and run their own cutting-edge models. This external validation proves that Google's approach is about making large-scale AI economically sensible.

The Vertical Advantage:

Google is the only major company that is competing fiercely and winning or coming close to the top in every critical layer of the AI stack:

1. Infrastructure (TPUs): Competing directly with NVIDIA on highly efficient, specialized AI silicon.
2. Foundation Models (Gemini, Imagen, Veo): Competing with OpenAI/Microsoft and Anthropic on core intelligence.
3. Applications (Nano Banana, AI Overviews): Integrating AI features into products that serve billions of users globally.

This end-to-end control, from the silicon chip to the final consumer application, provides a powerful strategic and economic advantage that is unmatched in the industry.

**3. The ChatGPT Myth and Search Dominance**

The idea that chatgpt will kill Google Search is a false narrative. Facebook, Instagram, TikTok, Reddit all were supposed to reduce google search queries. They have only grown. This new technology has made it much easier to ask any type or questions in any language. We were previously limited to what we would or could google. Now there are no limits. The more we know, the more questions we have and the more we search. Google search will be just fine.

I think ChatGPT will become another app on the phone where users will go to. I envision it as a personal assistant and less of search. But only time will tell.

Google was and will remain the gateway to the internet. The new AI business will be a net positive for Google by creating a new revenue stream through Google Cloud (GCP) and gemini features and subscriptions to its user base.

**4. The Financial Powerhouse and PE Hypothesis**

The fundamentals confirm this giant is firing on all cylinders.

* Net Income King: Alphabet's Trailing Twelve Months net income ending June 30, 2025, was $115.573 Billion, making it one of the most profitable companies in the world. This was more than MSFT $101.832 billion and APPL $99.280 billion
* Accelerating Triple-Threat Growth: All core segments - Google Cloud, Youtube and Google Search are growing at double-digit rates.

The core reason Google's Price-to-Earnings (PE) ratio is generally lower than many other tech companies is its revenue mix being heavily dominated by consumer advertising.

Simply put, investors are willing to pay a higher multiple (PE) for the more predictable, higher-margin, and rapidly growing recurring revenue streams typical of enterprise software and cloud platforms.

My hypothesis is with AI increasingly driving revenue through Google Cloud Platform (GCP), the enterprise segment will become a bigger component of Google's business mix, and hence, the company will earn a higher blended Price-to-Earnings (PE) ratio. This is because Enterprise and Cloud businesses are valued more highly, providing predictable, high-margin, recurring subscription revenue (SaaS), a financial profile superior to advertising. As this higher-multiple segment captures a greater share of Google's overall profit, the market will be forced to re-rate $GOOGL with a higher blended multiple, making the current valuation—which is depressed by the ad-centric multiple look like a significant undervaluation and a compelling investment opportunity.

TLDR : GOOGL is a generational buy. You're buying the best-in-class *present* (Search/Maps/YouTube), the scaled *near-future* (Waymo/GCP), and the *long-term future* (Quantum/AI Core Tech) at a discount.

https://preview.redd.it/h9doi0xnuywf1.png?width=1179&format=png&auto=webp&s=741748eadb6976d2ebf32a72f601343e6abc7d5c

I have a modest rig that allows me to run Qwen 3.5 27B or even 35B via Ollama. Qwen has been amazing to work with and I've been fine with the slow drip trade-off.

Then Google released Gemma4.

Its fast - like 4 or 9B fast. Accuracy and confidence wise, reminds me of that first release of Gemini Pro that could actually produce code that would run.

As a "local guy" this shift in useability and confidence for a small self hosted LLM reminded me of what Deepseek brought to the table years ago with the thinking capability.

Give it a go when you have a chance, and apply the settings that google recommends, it does make a difference (slightly slower but better)

I tried a few releases and this one worked the best for all the tests I threw at it with law interpretation, python, brainstorming & problem solving.

bjoernb/gemma4-26b-fast:latest (not affiliated with whoever made this)

in the next few days I'll start checking the abliterated versions to see how they stand with pentest & sysec tasks vs Qwen

For testing https://github.com/witness-taco/ollama-benchmark-ui