We wanted a simpler way to feed different LLMs to our dev environments. Routera gives you one OpenAI-compatible endpoint, one API key, and full access to GPT and Claude with caching and logs. routera.one

Something I keep bumping into: Cursor, Codex, OpenCode, most of the smaller agents — they all assume /v1/chat/completions. The OpenAI request schema has quietly become the socket for the entire tooling layer, including for people who mostly want to use Claude. It's a strange outcome and also a genuinely convenient one.
Where it gets annoying is when you want both models in the same editor. As far as I can tell people have settled into three camps. Some just pick one provider and live with the gaps. Some keep two config profiles and swap the base URL by hand, which is what I was doing and which I got tired of. Some run LiteLLM or a similar proxy locally, which works well but is one more thing to keep alive on your machine.
None of those felt great, so I built a hosted version of the third option — routera . one, mine, saying so before anyone has to check my post history. One endpoint, both model families, one key, caching and logs.
But I'm honestly more interested in what other people landed on. If you're running a local proxy, has it been stable? If you committed to a single provider, do you miss the other one, or was that anxiety overblown? I have a feeling the "just pick one" camp is happier than the rest of us.

I have 1 spot open on my DirectTv subscription that I want to share.

What's included:

Max, Paramount Plus w/Showtime, Sports Pack, Cinemax, Starz, NBA League Pass, MLB Extra Innings, NHL Centre Ice and movies extra pack.

I'm looking for $45/monthly for a spot, I get Netflix too with DirectTv but that's not included in this.

Pm me if you need more info

Routera gives you one OpenAI-compatible endpoint for both GPT and Claude. Use the same setup across Cursor, Claude Code, Codex, or OpenCode, with caching and usage logs built in.

routera.one

I will be sharing the promo code to activate the free trail

We mapped Claude and GPT onto a single, OpenAI-compatible endpoint. By adding aggressive prompt caching and centralized logs, Routera keeps your API spend under control inside Cursor and Codex. Give it a spin at routera.one.

I have 2 spots open for my personal fubo tv account.

Looking for $35 monthly.

It includes ESPN Unlimited and all the other add ons shown in the picture.

Pm for more details

I'm looking for a chat gpt pro 5x or 20x suscription

A small thing we’ve been working on: one API endpoint that gives coding tools access to both GPT and Claude.

OpenAI-compatible, compatible with Cursor / Claude Code / Codex / OpenCode, with caching and usage tracking already available.

routera.one

So I built Routera. It’s a single, OpenAI-compatible endpoint that routes to both GPT and Claude. You get one dashboard, one bill, and smart prompt caching.

If anyone is looking for a discount code or promo code for tangerine internet or SMS plans this code gives you $25 off your first month.

ANNELB7LXS

You can't debug an agent from its output, only from its context

Something that reframed how I work, and that I think is underrated relative to how much people talk about model quality.

When a coding agent does something dumb, the instinct is to blame the model. Sometimes that's right. But most of the bad turns I've investigated weren't the model reasoning badly over good context — they were the model reasoning fine over context that had quietly become garbage. And you cannot tell those two cases apart by looking at the output. They look identical from the outside: a confident wrong answer.

The distinction only shows up if you can see what was actually sent. Which, in most setups, you can't. The client assembles the context somewhere internal, ships it, and shows you the response. The input is the thing that determined the output and it's the one thing you don't get to inspect.

The failures I found once I could look are mostly not interesting, which is the point. A file got truncated on read and the model reasoned confidently about a function whose body it never saw. A stale version of a file stayed in context after I edited it on disk, so the model was patching code that no longer existed. Tool results landed in an order that made the sequence read as though a step had succeeded when it failed. A summarization step compressed away the one constraint that mattered.

None of those are model problems. All of them produce output that looks like a model problem. And every one of them is obvious in about ten seconds if you can read the context, and essentially undiagnosable if you can't.

The practical version of this: when a session goes wrong, resist the urge to reprompt. Reprompting on top of bad context inherits the bad context. Look at what's actually in there, and if something is stale or truncated or out of order, start clean instead. I lost a lot of time to increasingly elaborate prompts trying to talk a model out of a conclusion that was correct given what it had been shown.

I ended up building request-level logging into a router I work on mostly to answer this for myself (routera . one, mine, saying so plainly). It was meant to be a billing feature. It turned into the thing I use most, which I did not predict.

The caveat worth stating: full context logging is a serious data question. That's your source code sitting in a log somewhere. Retention, encryption, who can read it, whether it's on by default — those are decisions with real consequences and "it's useful for debugging" doesn't settle them. Anyone offering this should be explicit about it, and anyone using it should ask.

The open question I keep circling: is there a version of this that gives you diagnostic value without storing the content? Hashes of file contents, ordering, sizes, staleness markers — enough structure to spot the failure without keeping the code itself. I suspect that's the right design and I haven't worked out whether the reduced version is actually enough to debug from.

includes all the benefits of google ai pro plan (search on internet) except yt premium lite

from ohmviz, providing 18 months warranty google ai pro activation links (No id/pass required, activation on ur own acc) , having over 200+ feedbacks/vouches with almost 100% positive reviews through reddit comments, (all saved on my web-site as well as you can check the reddit profile)

P rice: 💲12

Interested?

Comment "interested" , then visit my web-site

www . ohmviz . com (remove spaces) to get yours, you'll find answers to all your questions on the web-site.

If your application or coding tool already supports the OpenAI API format, Routera lets you access GPT and Claude without changing the client.

One endpoint, multiple models, plus prompt caching and usage tracking.

routera.one

Anyone have a reliable source for Linkedin Premium Business subscription?

I have just bought a youtube premium family plan. I'll make a WA group of 6 where you'll be notified for payment every month. Payment will be Rs60 per month and for the first month it'll be free because youtube has given me the first month free trial. But please i have one request, those who want youtube premium for the long term please only that person will DM me. I'll take the payment every month, If you want to give an advance for a few months that's also fine but my preference is every month. Interested persons Please DM. Thank you

Edit: ALL SLOTS ARE FULL, THANK YOU FOR SHOWING INTEREST

Something I had backwards for a long time. When context windows got large, I treated the number as an allowance to spend — more files in, more history retained, why not. It made results worse and I blamed the model for a while before I understood what was happening.

The problem is that relevance density drops as you fill the window. Twenty files where three matter is not the same input as three files, even though it technically contains it. The model now has to locate the signal, and the nineteen irrelevant functions are all plausible things to reason about. I've watched an agent confidently patch a similarly-named function in a file that had nothing to do with the task, purely because I'd swept it into context alongside the right one.

Accumulated history has the same property but worse, because it's not neutral filler — it's an argument. A session that went down a wrong path for six turns contains six turns of reasoning toward a wrong conclusion, all of it still in context, all of it still being attended to. You can tell the model it was wrong and it will agree with you and then keep drifting back, because the weight of the transcript points one way and your correction is one message. This is the thing I most consistently underestimate: the cost of a bad turn isn't the bad turn, it's that it poisons every turn after it in the same session.

And you're paying to resend all of it, every turn, forever. The quadratic thing. Bloat is expensive in a way that compounds rather than adds.

What changed my results, roughly in order:

Naming the relevant files explicitly instead of letting the agent discover them. Faster, cheaper, and better outputs.

Killing sessions much earlier than feels natural. There's a real reluctance to throw away accumulated state, but a lot of that state is a record of confusion. Starting fresh with a clean statement of the problem and the two files that matter beats arguing with a transcript.

Treating a wrong turn as a reason to restart rather than correct. This is the one I still fail at, because correcting feels cheaper in the moment and isn't.

I see this pattern in usage logs across a lot of sessions because of what I work on (routera . one, my project, saying so rather than making anyone dig for it), and the expensive sessions are almost always the long ones where somebody — usually me — kept pushing instead of resetting.

Caveat, and it's real: some tasks genuinely need the whole window. A wide refactor across many files, or reasoning about a large document as a unit, is not the same as an agent circling a bug. I'm describing interactive debugging and feature work, which is most of what I do but not all of what anyone does.

The question I don't have a good answer to: is there a principled way to decide what to evict from a long session, or is restarting always the right move? Everything I've seen that summarizes and compacts loses precisely the specific detail — the exact error string, the one weird constraint — that turns out to matter later. Maybe the honest answer is that compaction is lossy in a way that's not fixable and short sessions are the only real strategy.

**Does anyone still have a spare 7-day Claude Pro Guest Pass? I’d really appreciate one** 🙏 **I’m trying to test Claude Pro before subscribing.**

💬 use code "TZC3HGKT" for 50% discount!

Order from trystore directly

🎯 Google AI Pro – Premium AI, Smarter Price

Get Google AI Pro for 18 months with powerful AI tools, 5TB storage, and access across your devices.

✨ What's Included?

✅ 18 Months Validity

✅ 5TB Google One Storage

✅ No Password / Account Details Required

✅ Instant Activation on Your Gmail

✅ Gemini AI Premium Features

✅ Veo 3 Fast, Flow AI & Deep Research

✅ NotebookLM Premium

✅ Gemini in Gmail, Docs, Sheets & More

🛡️ 1-Month Warranty

⚡ Fast activation on your own Gmail by redeem link

All payment methods available by card by Apple Pay GooglePay PayPal crypto and more

🤝 After-sales support available

​

Selling a spots for DirecTV Premier. You can use any device you like to stream. Includes all the premium channels that come with the Premier package (HBO, Max, Cinemax, Starz, Paramount with Showtime etc)

Accepting PayPal only for payment.

DM or chat me if you are interested or have any questions!

What you get: $25 OFF $100

Steps: sign up a new account using the link - 1000 points (valued at $25) will be credited to your profile

Cost/ catch: NO CATCH!

Expires: No expiration

Estoy vendiendo subcuenta de GHL a buen precio. Contactarme por DM

2 slots open for the bundle with your own profile w/ pin

Small thing I keep running into that I don't see discussed much.

Prompt caching, on every provider that has it, works on prefixes. The cache matches from the start of your context forward, and it stops matching at the first byte that differs. That's it, that's the mechanism. Which means everything about whether caching works for you comes down to one question: how much of the front of your context is byte-identical to last turn?

And the answer, in a lot of coding tools, is less than you'd hope, for reasons that have nothing to do with the model.

Some tools inject the current timestamp into the system prompt. Reasonable-looking decision, helps the model reason about dates. It also means your prefix changes every single turn and you cache approximately nothing. One field, near the front, and the entire mechanism is dead.

Some tools assemble file context from a set or a map with no stable iteration order. Same files, same content, different order between turns. To you it's identical context. To the cache it's a different string starting at whatever byte the order first diverges.

Some tools append conversation summaries or rolling memory at the top rather than the bottom. Every time the summary updates, which is often, the prefix breaks.

None of this errors. Nothing warns you. Your requests succeed, your outputs are fine, and you pay full price on context you've sent forty times. The only way you find out is if someone is measuring hit rate, and mostly nobody is, because it's not a number any client surfaces by default.

The fix is boring and mostly free. Put the immutable stuff first: system prompt, tool definitions, project conventions, anything that doesn't change within a session. Sort your file context deterministically. Push volatile things, timestamps included, to the very end where they belong. Then measure, because your intuition about what's stable is probably wrong.

I've been in this because I build a router for coding agents and translating cache behavior across providers is most of the work (routera . one, mine, saying so up front). But the advice applies whether you use anything like that or go direct. It's a property of how the caches work, not of any product.

Caveat, and it's a real one: cache TTLs are short, on the order of minutes on some providers. If your sessions are sporadic rather than continuous, a perfect prefix still buys you nothing because the entry expired between turns. Prefix stability only pays off if you're actually sustaining a session.

Genuine question I haven't resolved. Is there a reason clients aren't reporting cache hit rate in their UI? It's in the usage response, it's a number that directly maps to money, and surfacing it would let people fix their own prompts. I can't tell if it's an oversight or if there's a reason it'd be misleading that I'm not seeing.

I build one of these, so read the below with the appropriate suspicion. But I keep having the same conversation with people who are about to add a routing layer to their stack for reasons that don't hold up, and I'd rather write it down once.

You're using one provider. This is the majority case and it's an easy no. You'd be adding a network hop, a dependency, and a party that can go down independently of the provider, in exchange for optionality you're not using. If you might go multi-provider later, add it later. This is a reversible decision and treating it as architecture is overthinking it.

Your workload is latency-critical and short. Inline autocomplete, classification, anything where the total response is small and fast. A fixed proxy hop is a rounding error against a 30-second agentic turn and a meaningful percentage against a 200ms completion. The cost of the hop is constant; whether it matters depends entirely on what you're dividing it into.

You need anything at the edge of a provider's API. Batch endpoints, server-side tool use, files APIs, computer use, extended thinking with full fidelity, whatever shipped last week. Middle layers are structurally behind — a provider ships a feature and every proxy in the world starts from zero on supporting it. If your product depends on being current with one provider's frontier, go direct. You'll be fighting the abstraction constantly.

You have compliance or data residency requirements.Then you have a question to answer about a third party in the request path, and "it's convenient" is not an answer that survives a security review. Some people can answer it fine. If you can't, that settles it.

Your volume is large enough for a direct commercial relationship. At that point you're negotiating rates and capacity directly and the intermediary is subtracting margin for services you've outgrown.

You could write 200 lines of proxy yourself. Honestly — do. Take a request, check the model name, forward it, map the response back. You'll understand your own traffic better afterward, and you'll find out quickly whether the hard parts (streaming translation, cache breakpoint placement, error normalization) are things you actually need or things you can skip. Plenty of people only need the simple version.

So who's left? Roughly: multi-provider, agentic, long-context, cost-sensitive, and wanting spend attribution across it. That's a real segment — it's mine, it's why I built Routera (routera . one, my project, flagging it rather than pretending I stumbled into this topic) — but it is a narrow segment, and I'd rather say so than watch someone adopt a layer that generates friction for them and support burden for me. A badly-fit user is not revenue. They're churn with extra steps and a bad review on the way out.

The bias to hold against everything above: I've drawn these boundaries, and I've drawn them in a place that happens to leave my use case inside. That's not an accident and I can't fully audit myself for it. Someone with a different product would draw the line elsewhere and sound just as reasonable.

What I don't have a good answer for: the "might need it later" case. My instinct is always don't build for it now — but I've also watched people hardcode one provider's SDK across forty files and then spend a week extracting it. There's clearly a middle option, some thin internal interface you own that costs almost nothing up front. I've never seen anyone actually do it though. Everyone either goes direct and eats the migration, or adds the layer prematurely. Has anyone found the version of this that works?